Time_Series_Forecasting.ipynb

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            "Collecting AutoTS\n",
            "  Downloading autots-0.6.1-py3-none-any.whl (777 kB)\n",
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            "\u001b[?25hRequirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.10/dist-packages (from AutoTS) (1.23.5)\n",
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            "Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.10.0->AutoTS) (0.5.3)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels>=0.10.0->AutoTS) (23.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.2->statsmodels>=0.10.0->AutoTS) (1.16.0)\n",
            "Installing collected packages: AutoTS\n",
            "Successfully installed AutoTS-0.6.1\n"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "np.random.seed(42)\n",
        "N = 100\n",
        "rng = pd.date_range('2019-01-01', freq='D', periods=N)\n",
        "df = pd.DataFrame(np.random.rand(N, 1), columns=['value'], index=rng)\n",
        "df['value'][::7] = 10"
      ]
    },
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            "text/plain": [
              "                value\n",
              "2019-01-01  10.000000\n",
              "2019-01-02   0.950714\n",
              "2019-01-03   0.731994\n",
              "2019-01-04   0.598658\n",
              "2019-01-05   0.156019\n",
              "...               ...\n",
              "2019-04-06   0.493796\n",
              "2019-04-07   0.522733\n",
              "2019-04-08   0.427541\n",
              "2019-04-09  10.000000\n",
              "2019-04-10   0.107891\n",
              "\n",
              "[100 rows x 1 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
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    {
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      "execution_count": null,
      "metadata": {
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      "source": []
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Data frequency is: D, used frequency is: D\n",
            "Model Number: 1 with model ARIMA in generation 0 of 0\n",
            "Model Number: 2 with model AverageValueNaive in generation 0 of 0\n",
            "Model Number: 3 with model AverageValueNaive in generation 0 of 0\n",
            "Model Number: 4 with model AverageValueNaive in generation 0 of 0\n",
            "Model Number: 5 with model DatepartRegression in generation 0 of 0\n",
            "Model Number: 6 with model DatepartRegression in generation 0 of 0\n",
            "Model Number: 7 with model DatepartRegression in generation 0 of 0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        },
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          "text": [
            "Model Number: 8 with model DatepartRegression in generation 0 of 0\n",
            "Epoch 1/50\n",
            "3/3 [==============================] - 11s 11ms/step - loss: 0.3624\n",
            "Epoch 2/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3576\n",
            "Epoch 3/50\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.3553\n",
            "Epoch 4/50\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 0.3490\n",
            "Epoch 5/50\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.3478\n",
            "Epoch 6/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3478\n",
            "Epoch 7/50\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.3440\n",
            "Epoch 8/50\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.3461\n",
            "Epoch 9/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3448\n",
            "Epoch 10/50\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.3391\n",
            "Epoch 11/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3427\n",
            "Epoch 12/50\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.3409\n",
            "Epoch 13/50\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.3408\n",
            "Epoch 14/50\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.3411\n",
            "Epoch 15/50\n",
            "3/3 [==============================] - 0s 7ms/step - loss: 0.3338\n",
            "Epoch 16/50\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.3416\n",
            "Epoch 17/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3385\n",
            "Epoch 18/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3422\n",
            "Epoch 19/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3431\n",
            "Epoch 20/50\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.3416\n",
            "Epoch 21/50\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.3407\n",
            "Epoch 22/50\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.3328\n",
            "Epoch 23/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3385\n",
            "Epoch 24/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3342\n",
            "Epoch 25/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3390\n",
            "Epoch 26/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3368\n",
            "Epoch 27/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3381\n",
            "Epoch 28/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3337\n",
            "Epoch 29/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3389\n",
            "Epoch 30/50\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 0.3339\n",
            "Epoch 31/50\n",
            "3/3 [==============================] - 0s 10ms/step - loss: 0.3397\n",
            "Epoch 32/50\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 0.3391\n",
            "Epoch 33/50\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 0.3359\n",
            "Epoch 34/50\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.3405\n",
            "Epoch 35/50\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.3355\n",
            "Epoch 36/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3346\n",
            "Epoch 37/50\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.3291\n",
            "Epoch 38/50\n",
            "3/3 [==============================] - 0s 8ms/step - loss: 0.3392\n",
            "Epoch 39/50\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.3373\n",
            "Epoch 40/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3347\n",
            "Epoch 41/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3342\n",
            "Epoch 42/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3337\n",
            "Epoch 43/50\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.3354\n",
            "Epoch 44/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3399\n",
            "Epoch 45/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3288\n",
            "Epoch 46/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3392\n",
            "Epoch 47/50\n",
            "3/3 [==============================] - 0s 5ms/step - loss: 0.3277\n",
            "Epoch 48/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3298\n",
            "Epoch 49/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3338\n",
            "Epoch 50/50\n",
            "3/3 [==============================] - 0s 6ms/step - loss: 0.3325\n",
            "1/1 [==============================] - 1s 1s/step\n",
            "Model Number: 9 with model ETS in generation 0 of 0\n",
            "Model Number: 10 with model ETS in generation 0 of 0\n",
            "Model Number: 11 with model GLM in generation 0 of 0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/families/links.py:198: RuntimeWarning: overflow encountered in exp\n",
            "  t = np.exp(-z)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Number: 12 with model GLM in generation 0 of 0\n",
            "Model Number: 13 with model GLS in generation 0 of 0\n",
            "Model Number: 14 with model GLS in generation 0 of 0\n",
            "Model Number: 15 with model LastValueNaive in generation 0 of 0\n",
            "Model Number: 16 with model LastValueNaive in generation 0 of 0\n",
            "Model Number: 17 with model LastValueNaive in generation 0 of 0\n",
            "Model Number: 18 with model LastValueNaive in generation 0 of 0\n",
            "Model Number: 19 with model SeasonalNaive in generation 0 of 0\n",
            "Model Number: 20 with model SeasonalNaive in generation 0 of 0\n",
            "Model Number: 21 with model SeasonalNaive in generation 0 of 0\n",
            "Model Number: 22 with model UnobservedComponents in generation 0 of 0\n",
            "Model Number: 23 with model UnobservedComponents in generation 0 of 0\n",
            "Model Number: 24 with model UnobservedComponents in generation 0 of 0\n",
            "Model Number: 25 with model ConstantNaive in generation 0 of 0\n",
            "Model Number: 26 with model FBProphet in generation 0 of 0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpki_a5ecw/d83tkwbw.json\n",
            "DEBUG:cmdstanpy:input tempfile: /tmp/tmpki_a5ecw/jj2zwxyx.json\n",
            "DEBUG:cmdstanpy:idx 0\n",
            "DEBUG:cmdstanpy:running CmdStan, num_threads: None\n",
            "DEBUG:cmdstanpy:CmdStan args: ['/usr/local/lib/python3.10/dist-packages/prophet/stan_model/prophet_model.bin', 'random', 'seed=7879', 'data', 'file=/tmp/tmpki_a5ecw/d83tkwbw.json', 'init=/tmp/tmpki_a5ecw/jj2zwxyx.json', 'output', 'file=/tmp/tmpki_a5ecw/prophet_modelsw6t7lwh/prophet_model-20231023015700.csv', 'method=optimize', 'algorithm=newton', 'iter=10000']\n",
            "01:57:00 - cmdstanpy - INFO - Chain [1] start processing\n",
            "INFO:cmdstanpy:Chain [1] start processing\n",
            "01:57:01 - cmdstanpy - INFO - Chain [1] done processing\n",
            "INFO:cmdstanpy:Chain [1] done processing\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Number: 27 with model DatepartRegression in generation 0 of 0\n",
            "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 27 in generation 0: DatepartRegression\n",
            "Model Number: 28 with model SeasonalNaive in generation 0 of 0\n",
            "Model Number: 29 with model DatepartRegression in generation 0 of 0\n",
            "Model Number: 30 with model UnobservedComponents in generation 0 of 0\n",
            "Model Number: 31 with model UnobservedComponents in generation 0 of 0\n",
            "Model Number: 32 with model ETS in generation 0 of 0\n",
            "Model Number: 33 with model ARDL in generation 0 of 0\n",
            "Model Number: 34 with model UnivariateMotif in generation 0 of 0\n",
            "Model Number: 35 with model UnivariateMotif in generation 0 of 0\n",
            "Model Number: 36 with model FBProphet in generation 0 of 0\n",
            "Model Number: 37 with model SeasonalNaive in generation 0 of 0\n",
            "Model Number: 38 with model DatepartRegression in generation 0 of 0\n",
            "Model Number: 39 with model Theta in generation 0 of 0\n",
            "Model Number: 40 with model UnivariateRegression in generation 0 of 0\n",
            "Template Eval Error: ValueError('Input X contains NaN.\\nDecisionTreeRegressor does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values') in model 40 in generation 0: UnivariateRegression\n",
            "Model Number: 41 with model ARCH in generation 0 of 0\n",
            "Template Eval Error: ImportError('`arch` package must be installed from pip') in model 41 in generation 0: ARCH\n",
            "Model Number: 42 with model SeasonalityMotif in generation 0 of 0\n",
            "Model Number: 43 with model Theta in generation 0 of 0\n",
            "Model Number: 44 with model MLEnsemble in generation 0 of 0\n",
            "Template Eval Error: ValueError('all the input array dimensions for the concatenation axis must match exactly, but along dimension 2, the array at index 0 has size 1 and the array at index 1 has size 21') in model 44 in generation 0: MLEnsemble\n",
            "Model Number: 45 with model ConstantNaive in generation 0 of 0\n",
            "Model Number: 46 with model PreprocessingRegression in generation 0 of 0\n",
            "Template Eval Error: IndexError('index -905 is out of bounds for axis 0 with size 90') in model 46 in generation 0: PreprocessingRegression\n",
            "Model Number: 47 with model DatepartRegression in generation 0 of 0\n",
            "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 47 in generation 0: DatepartRegression\n",
            "Model Number: 48 with model FBProphet in generation 0 of 0\n",
            "Model Number: 49 with model UnobservedComponents in generation 0 of 0\n",
            "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor supplied\") in model 49 in generation 0: UnobservedComponents\n",
            "Model Number: 50 with model UnivariateMotif in generation 0 of 0\n",
            "Template Eval Error: ValueError('kth(=100) out of bounds (71)') in model 50 in generation 0: UnivariateMotif\n",
            "Model Number: 51 with model ARCH in generation 0 of 0\n",
            "Template Eval Error: ImportError('`arch` package must be installed from pip') in model 51 in generation 0: ARCH\n",
            "Model Number: 52 with model ARIMA in generation 0 of 0\n",
            "Model Number: 53 with model KalmanStateSpace in generation 0 of 0\n",
            "Model Number: 54 with model UnivariateRegression in generation 0 of 0\n",
            "Template Eval Error: ValueError(\"regression_type='User' but not future_regressor supplied.\") in model 54 in generation 0: UnivariateRegression\n",
            "Model Number: 55 with model AverageValueNaive in generation 0 of 0\n",
            "Model Number: 56 with model MetricMotif in generation 0 of 0\n",
            "Model Number: 57 with model ETS in generation 0 of 0\n",
            "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 57 in generation 0: ETS\n",
            "Model Number: 58 with model LastValueNaive in generation 0 of 0\n",
            "Model Number: 59 with model SeasonalityMotif in generation 0 of 0\n",
            "Template Eval Error: Exception('Transformer KalmanSmoothing failed on fit') in model 59 in generation 0: SeasonalityMotif\n",
            "Model Number: 60 with model SeasonalNaive in generation 0 of 0\n",
            "Template Eval Error: Exception('Transformer HolidayTransformer failed on fit') in model 60 in generation 0: SeasonalNaive\n",
            "Model Number: 61 with model GLS in generation 0 of 0\n",
            "Model Number: 62 with model NeuralProphet in generation 0 of 0\n",
            "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 62 in generation 0: NeuralProphet\n",
            "Model Number: 63 with model ARDL in generation 0 of 0\n",
            "Template Eval Error: ValueError(\"regression_type='User' but future_regressor not supplied\") in model 63 in generation 0: ARDL\n",
            "Model Number: 64 with model GLM in generation 0 of 0\n",
            "Template Eval Error: ValueError('NaN, inf or invalid value detected in weights, estimation infeasible.') in model 64 in generation 0: GLM\n",
            "Model Number: 65 with model NeuralProphet in generation 0 of 0\n",
            "Template Eval Error: ModuleNotFoundError(\"No module named 'neuralprophet'\") in model 65 in generation 0: NeuralProphet\n",
            "Model Number: 66 with model LastValueNaive in generation 0 of 0\n",
            "Model Number: 67 with model GLS in generation 0 of 0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/families/family.py:445: RuntimeWarning: divide by zero encountered in divide\n",
            "  endog_mu = self._clean(endog / mu)\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/families/family.py:143: RuntimeWarning: divide by zero encountered in divide\n",
            "  return 1. / (self.link.deriv(mu)**2 * self.variance(mu))\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Number: 68 with model ARIMA in generation 0 of 0\n",
            "Model Number: 69 with model ARCH in generation 0 of 0\n",
            "Template Eval Error: ImportError('`arch` package must be installed from pip') in model 69 in generation 0: ARCH\n",
            "Model Number: 70 with model SeasonalityMotif in generation 0 of 0\n",
            "Model Number: 71 with model PreprocessingRegression in generation 0 of 0\n",
            "Template Eval Error: ValueError('all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 0 and the array at index 1 has size 90') in model 71 in generation 0: PreprocessingRegression\n",
            "Model Number: 72 with model ARCH in generation 0 of 0\n",
            "Template Eval Error: ImportError('`arch` package must be installed from pip') in model 72 in generation 0: ARCH\n",
            "Model Number: 73 with model AverageValueNaive in generation 0 of 0\n",
            "Model Number: 74 with model GLS in generation 0 of 0\n",
            "Model Number: 75 with model Theta in generation 0 of 0\n",
            "Model Number: 76 with model DatepartRegression in generation 0 of 0\n",
            "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor passed\") in model 76 in generation 0: DatepartRegression\n",
            "Model Number: 77 with model GLM in generation 0 of 0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/generalized_linear_model.py:307: DomainWarning: The InversePower link function does not respect the domain of the Gamma family.\n",
            "  warnings.warn((f\"The {type(family.link).__name__} link function \"\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/families/family.py:143: RuntimeWarning: overflow encountered in square\n",
            "  return 1. / (self.link.deriv(mu)**2 * self.variance(mu))\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/families/links.py:325: RuntimeWarning: divide by zero encountered in power\n",
            "  return np.power(z, 1. / self.power)\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/families/family.py:775: RuntimeWarning: invalid value encountered in divide\n",
            "  resid_dev = -np.log(endog_mu) + (endog - mu) / mu\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/generalized_linear_model.py:813: RuntimeWarning: invalid value encountered in divide\n",
            "  return np.sum(resid / self.family.variance(mu)) / self.df_resid\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/families/family.py:143: RuntimeWarning: invalid value encountered in multiply\n",
            "  return 1. / (self.link.deriv(mu)**2 * self.variance(mu))\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/generalized_linear_model.py:1243: RuntimeWarning: invalid value encountered in multiply\n",
            "  wlsendog = (lin_pred + self.family.link.deriv(mu) * (self.endog-mu)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Template Eval Error: ValueError('NaN, inf or invalid value detected in weights, estimation infeasible.') in model 77 in generation 0: GLM\n",
            "Model Number: 78 with model UnivariateRegression in generation 0 of 0\n",
            "Template Eval Error: ValueError('Input X contains NaN.\\nRidge does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values') in model 78 in generation 0: UnivariateRegression\n",
            "Model Number: 79 with model MLEnsemble in generation 0 of 0\n",
            "Template Eval Error: Exception('Transformer AnomalyRemoval failed on fit') in model 79 in generation 0: MLEnsemble\n",
            "Model Number: 80 with model GLM in generation 0 of 0\n",
            "Template Eval Error: ValueError('NaN, inf or invalid value detected in weights, estimation infeasible.') in model 80 in generation 0: GLM\n",
            "Model Number: 81 with model ETS in generation 0 of 0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/families/links.py:527: RuntimeWarning: overflow encountered in exp\n",
            "  return np.exp(z)\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/families/family.py:445: RuntimeWarning: divide by zero encountered in divide\n",
            "  endog_mu = self._clean(endog / mu)\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/families/family.py:143: RuntimeWarning: invalid value encountered in multiply\n",
            "  return 1. / (self.link.deriv(mu)**2 * self.variance(mu))\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/families/family.py:143: RuntimeWarning: divide by zero encountered in divide\n",
            "  return 1. / (self.link.deriv(mu)**2 * self.variance(mu))\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/generalized_linear_model.py:1243: RuntimeWarning: invalid value encountered in multiply\n",
            "  wlsendog = (lin_pred + self.family.link.deriv(mu) * (self.endog-mu)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.25643e-24): result may not be accurate.\n",
            "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Number: 82 with model FBProphet in generation 0 of 0\n",
            "Model Number: 83 with model Theta in generation 0 of 0\n",
            "Model Number: 84 with model ARDL in generation 0 of 0\n",
            "Template Eval Error: Exception('Transformer HolidayTransformer failed on fit') in model 84 in generation 0: ARDL\n",
            "Model Number: 85 with model MLEnsemble in generation 0 of 0\n",
            "Template Eval Error: ValueError('all the input array dimensions for the concatenation axis must match exactly, but along dimension 2, the array at index 0 has size 1 and the array at index 1 has size 21') in model 85 in generation 0: MLEnsemble\n",
            "Model Number: 86 with model ARIMA in generation 0 of 0\n",
            "Template Eval Error: Exception('Transformer HolidayTransformer failed on fit') in model 86 in generation 0: ARIMA\n",
            "Model Number: 87 with model LastValueNaive in generation 0 of 0\n",
            "Model Number: 88 with model UnivariateMotif in generation 0 of 0\n",
            "Model Number: 89 with model NeuralProphet in generation 0 of 0\n",
            "Template Eval Error: ModuleNotFoundError(\"No module named 'neuralprophet'\") in model 89 in generation 0: NeuralProphet\n",
            "Model Number: 90 with model ARCH in generation 0 of 0\n",
            "Template Eval Error: ImportError('`arch` package must be installed from pip') in model 90 in generation 0: ARCH\n",
            "Model Number: 91 with model UnobservedComponents in generation 0 of 0\n",
            "Model Number: 92 with model UnobservedComponents in generation 0 of 0\n",
            "Model Number: 93 with model ARIMA in generation 0 of 0\n",
            "Model Number: 94 with model SeasonalityMotif in generation 0 of 0\n",
            "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 94 in generation 0: SeasonalityMotif\n",
            "Model Number: 95 with model NeuralProphet in generation 0 of 0\n",
            "Template Eval Error: ModuleNotFoundError(\"No module named 'neuralprophet'\") in model 95 in generation 0: NeuralProphet\n",
            "Model Number: 96 with model UnivariateMotif in generation 0 of 0\n",
            "Model Number: 97 with model ARCH in generation 0 of 0\n",
            "Template Eval Error: ImportError('`arch` package must be installed from pip') in model 97 in generation 0: ARCH\n",
            "Model Number: 98 with model ARCH in generation 0 of 0\n",
            "Template Eval Error: Exception('Transformer ScipyFilter failed on fit') in model 98 in generation 0: ARCH\n",
            "Model Number: 99 with model AverageValueNaive in generation 0 of 0\n",
            "Model Number: 100 with model UnivariateRegression in generation 0 of 0\n",
            "Model Number: 101 with model GLS in generation 0 of 0\n",
            "Model Number: 102 with model SeasonalityMotif in generation 0 of 0\n",
            "Model Number: 103 with model GLM in generation 0 of 0\n",
            "Template Eval Error: Exception('Transformer Detrend failed on fit') in model 103 in generation 0: GLM\n",
            "Model Number: 104 with model ARDL in generation 0 of 0\n",
            "Model Number: 105 with model MLEnsemble in generation 0 of 0\n",
            "Template Eval Error: ValueError('all the input array dimensions for the concatenation axis must match exactly, but along dimension 2, the array at index 0 has size 1 and the array at index 1 has size 21') in model 105 in generation 0: MLEnsemble\n",
            "Model Number: 106 with model SeasonalNaive in generation 0 of 0\n",
            "Model Number: 107 with model PreprocessingRegression in generation 0 of 0\n",
            "Template Eval Error: ValueError('all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 70 and the array at index 1 has size 90') in model 107 in generation 0: PreprocessingRegression\n",
            "Model Number: 108 with model ConstantNaive in generation 0 of 0\n",
            "Model Number: 109 with model ARIMA in generation 0 of 0\n",
            "Template Eval Error: Exception('Transformer KalmanSmoothing failed on fit') in model 109 in generation 0: ARIMA\n",
            "Model Number: 110 with model SeasonalNaive in generation 0 of 0\n",
            "Model Number: 111 with model MLEnsemble in generation 0 of 0\n",
            "Template Eval Error: ValueError('all the input array dimensions for the concatenation axis must match exactly, but along dimension 2, the array at index 0 has size 1 and the array at index 1 has size 21') in model 111 in generation 0: MLEnsemble\n",
            "Model Number: 112 with model FBProphet in generation 0 of 0\n",
            "Model Number: 113 with model SeasonalNaive in generation 0 of 0\n",
            "Model Number: 114 with model UnivariateRegression in generation 0 of 0\n",
            "Template Eval Error: ValueError(\"regression_type='User' but not future_regressor supplied.\") in model 114 in generation 0: UnivariateRegression\n",
            "Model Number: 115 with model MLEnsemble in generation 0 of 0\n",
            "Template Eval Error: ValueError('all the input array dimensions for the concatenation axis must match exactly, but along dimension 2, the array at index 0 has size 1 and the array at index 1 has size 21') in model 115 in generation 0: MLEnsemble\n",
            "Model Number: 116 with model UnobservedComponents in generation 0 of 0\n",
            "Template Eval Error: Exception('Transformer HolidayTransformer failed on fit') in model 116 in generation 0: UnobservedComponents\n",
            "Model Number: 117 with model FBProphet in generation 0 of 0\n",
            "Model Number: 118 with model ARDL in generation 0 of 0\n",
            "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 118 in generation 0: ARDL\n",
            "Model Number: 119 with model ARDL in generation 0 of 0\n",
            "Model Number: 120 with model MetricMotif in generation 0 of 0\n",
            "Model Number: 121 with model ConstantNaive in generation 0 of 0\n",
            "Template Eval Error: ValueError(\"Model returned NaN due to a preprocessing transformer {'fillna': 'ffill', 'transformations': {'0': 'AlignLastValue', '1': 'MaxAbsScaler', '2': 'Round', '3': 'Detrend'}, 'transformation_params': {'0': {'rows': 1, 'lag': 1, 'method': 'multiplicative', 'strength': 1.0, 'first_value_only': False}, '1': {}, '2': {'decimals': -1, 'on_transform': False, 'on_inverse': True}, '3': {'model': 'Linear', 'phi': 1, 'window': None, 'transform_dict': None}}}. fail_on_forecast_nan=True\") in model 121 in generation 0: ConstantNaive\n",
            "Model Number: 122 with model ARDL in generation 0 of 0\n",
            "Template Eval Error: ValueError(\"ARDL series value failed with error ValueError('The number of regressors (176) including deterministics, lags of the endog, lags of the exogenous, and fixed regressors is larger than the sample available for estimation (88).') exog train             weekend  quarter      epoch  month_1  month_2  month_3  month_4  \\\\\\n2019-01-01        0        1  2458484.5      1.0      0.0      0.0      0.0   \\n2019-01-02        0        1  2458485.5      1.0      0.0      0.0      0.0   \\n2019-01-03        0        1  2458486.5      1.0      0.0      0.0      0.0   \\n2019-01-04        0        1  2458487.5      1.0      0.0      0.0      0.0   \\n2019-01-05        1        1  2458488.5      1.0      0.0      0.0      0.0   \\n...             ...      ...        ...      ...      ...      ...      ...   \\n2019-03-27        0        1  2458569.5      0.0      0.0      1.0      0.0   \\n2019-03-28        0        1  2458570.5      0.0      0.0      1.0      0.0   \\n2019-03-29        0        1  2458571.5      0.0      0.0      1.0      0.0   \\n2019-03-30        1        1  2458572.5      0.0      0.0      1.0      0.0   \\n2019-03-31        1        1  2458573.5      0.0      0.0      1.0      0.0   \\n\\n            month_5  month_6  month_7  ...  day_27  day_28  day_29  day_30  \\\\\\n2019-01-01      0.0      0.0      0.0  ...     0.0     0.0     0.0     0.0   \\n2019-01-02      0.0      0.0      0.0  ...     0.0     0.0     0.0     0.0   \\n2019-01-03      0.0      0.0      0.0  ...     0.0     0.0     0.0     0.0   \\n2019-01-04      0.0      0.0      0.0  ...     0.0     0.0     0.0     0.0   \\n2019-01-05      0.0      0.0      0.0  ...     0.0     0.0     0.0     0.0   \\n...             ...      ...      ...  ...     ...     ...     ...     ...   \\n2019-03-27      0.0      0.0      0.0  ...     1.0     0.0     0.0     0.0   \\n2019-03-28      0.0      0.0      0.0  ...     0.0     1.0     0.0     0.0   \\n2019-03-29      0.0      0.0      0.0  ...     0.0     0.0     1.0     0.0   \\n2019-03-30      0.0      0.0      0.0  ...     0.0     0.0     0.0     1.0   \\n2019-03-31      0.0      0.0      0.0  ...     0.0     0.0     0.0     0.0   \\n\\n            day_31  weekdayofmonth_1  weekdayofmonth_2  weekdayofmonth_3  \\\\\\n2019-01-01     0.0               1.0               0.0               0.0   \\n2019-01-02     0.0               1.0               0.0               0.0   \\n2019-01-03     0.0               1.0               0.0               0.0   \\n2019-01-04     0.0               1.0               0.0               0.0   \\n2019-01-05     0.0               1.0               0.0               0.0   \\n...            ...               ...               ...               ...   \\n2019-03-27     0.0               0.0               0.0               0.0   \\n2019-03-28     0.0               0.0               0.0               0.0   \\n2019-03-29     0.0               0.0               0.0               0.0   \\n2019-03-30     0.0               0.0               0.0               0.0   \\n2019-03-31     1.0               0.0               0.0               0.0   \\n\\n            weekdayofmonth_4  weekdayofmonth_5  \\n2019-01-01               0.0               0.0  \\n2019-01-02               0.0               0.0  \\n2019-01-03               0.0               0.0  \\n2019-01-04               0.0               0.0  \\n2019-01-05               0.0               0.0  \\n...                      ...               ...  \\n2019-03-27               1.0               0.0  \\n2019-03-28               1.0               0.0  \\n2019-03-29               0.0               1.0  \\n2019-03-30               0.0               1.0  \\n2019-03-31               0.0               1.0  \\n\\n[90 rows x 58 columns] and predict             weekend  quarter      epoch  month_1  month_2  month_3  month_4  \\\\\\n2019-04-01        0        2  2458574.5      0.0      0.0      0.0      1.0   \\n2019-04-02        0        2  2458575.5      0.0      0.0      0.0      1.0   \\n2019-04-03        0        2  2458576.5      0.0      0.0      0.0      1.0   \\n2019-04-04        0        2  2458577.5      0.0      0.0      0.0      1.0   \\n2019-04-05        0        2  2458578.5      0.0      0.0      0.0      1.0   \\n2019-04-06        1        2  2458579.5      0.0      0.0      0.0      1.0   \\n2019-04-07        1        2  2458580.5      0.0      0.0      0.0      1.0   \\n2019-04-08        0        2  2458581.5      0.0      0.0      0.0      1.0   \\n2019-04-09        0        2  2458582.5      0.0      0.0      0.0      1.0   \\n2019-04-10        0        2  2458583.5      0.0      0.0      0.0      1.0   \\n\\n            month_5  month_6  month_7  ...  day_27  day_28  day_29  day_30  \\\\\\n2019-04-01      0.0      0.0      0.0  ...     0.0     0.0     0.0     0.0   \\n2019-04-02      0.0      0.0      0.0  ...     0.0     0.0     0.0     0.0   \\n2019-04-03      0.0      0.0      0.0  ...     0.0     0.0     0.0     0.0   \\n2019-04-04      0.0      0.0      0.0  ...     0.0     0.0     0.0     0.0   \\n2019-04-05      0.0      0.0      0.0  ...     0.0     0.0     0.0     0.0   \\n2019-04-06      0.0      0.0      0.0  ...     0.0     0.0     0.0     0.0   \\n2019-04-07      0.0      0.0      0.0  ...     0.0     0.0     0.0     0.0   \\n2019-04-08      0.0      0.0      0.0  ...     0.0     0.0     0.0     0.0   \\n2019-04-09      0.0      0.0      0.0  ...     0.0     0.0     0.0     0.0   \\n2019-04-10      0.0      0.0      0.0  ...     0.0     0.0     0.0     0.0   \\n\\n            day_31  weekdayofmonth_1  weekdayofmonth_2  weekdayofmonth_3  \\\\\\n2019-04-01     0.0               1.0               0.0               0.0   \\n2019-04-02     0.0               1.0               0.0               0.0   \\n2019-04-03     0.0               1.0               0.0               0.0   \\n2019-04-04     0.0               1.0               0.0               0.0   \\n2019-04-05     0.0               1.0               0.0               0.0   \\n2019-04-06     0.0               1.0               0.0               0.0   \\n2019-04-07     0.0               1.0               0.0               0.0   \\n2019-04-08     0.0               0.0               1.0               0.0   \\n2019-04-09     0.0               0.0               1.0               0.0   \\n2019-04-10     0.0               0.0               1.0               0.0   \\n\\n            weekdayofmonth_4  weekdayofmonth_5  \\n2019-04-01               0.0               0.0  \\n2019-04-02               0.0               0.0  \\n2019-04-03               0.0               0.0  \\n2019-04-04               0.0               0.0  \\n2019-04-05               0.0               0.0  \\n2019-04-06               0.0               0.0  \\n2019-04-07               0.0               0.0  \\n2019-04-08               0.0               0.0  \\n2019-04-09               0.0               0.0  \\n2019-04-10               0.0               0.0  \\n\\n[10 rows x 58 columns]\") in model 122 in generation 0: ARDL\n",
            "Model Number: 123 with model MLEnsemble in generation 0 of 0\n",
            "Template Eval Error: Exception('Transformer HolidayTransformer failed on fit') in model 123 in generation 0: MLEnsemble\n",
            "Model Number: 124 with model SeasonalityMotif in generation 0 of 0\n",
            "Model Number: 125 with model UnivariateMotif in generation 0 of 0\n",
            "Model Number: 126 with model ETS in generation 0 of 0\n",
            "Model Number: 127 with model LastValueNaive in generation 0 of 0\n",
            "Model Number: 128 with model AverageValueNaive in generation 0 of 0\n",
            "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 128 in generation 0: AverageValueNaive\n",
            "Model Number: 129 with model UnivariateMotif in generation 0 of 0\n",
            "Template Eval Error: Exception('Transformer StandardScaler failed on fit') in model 129 in generation 0: UnivariateMotif\n",
            "Model Number: 130 with model FBProphet in generation 0 of 0\n",
            "Model Number: 131 with model MLEnsemble in generation 0 of 0\n",
            "Template Eval Error: ValueError('all the input array dimensions for the concatenation axis must match exactly, but along dimension 2, the array at index 0 has size 1 and the array at index 1 has size 21') in model 131 in generation 0: MLEnsemble\n",
            "Model Number: 132 with model SeasonalNaive in generation 0 of 0\n",
            "Model Number: 133 with model GLM in generation 0 of 0\n",
            "Template Eval Error: ValueError('regression_type=user and no future_regressor passed') in model 133 in generation 0: GLM\n",
            "Model Number: 134 with model KalmanStateSpace in generation 0 of 0\n",
            "Model Number: 135 with model ARIMA in generation 0 of 0\n",
            "Model Number: 136 with model DatepartRegression in generation 0 of 0\n",
            "Model Number: 137 with model Theta in generation 0 of 0\n",
            "Model Number: 138 with model UnobservedComponents in generation 0 of 0\n",
            "Model Number: 139 with model SeasonalityMotif in generation 0 of 0\n",
            "Model Number: 140 with model UnobservedComponents in generation 0 of 0\n",
            "Template Eval Error: ValueError(\"regression_type='User' but no future_regressor supplied\") in model 140 in generation 0: UnobservedComponents\n",
            "Model Number: 141 with model AverageValueNaive in generation 0 of 0\n",
            "Template Eval Error: Exception('Transformer MaxAbsScaler failed on inverse') in model 141 in generation 0: AverageValueNaive\n",
            "Model Number: 142 with model LastValueNaive in generation 0 of 0\n",
            "Model Number: 143 with model GLM in generation 0 of 0\n",
            "Model Number: 144 with model UnobservedComponents in generation 0 of 0\n",
            "Template Eval Error: Exception('Transformer AlignLastValue failed on inverse') in model 144 in generation 0: UnobservedComponents\n",
            "Model Number: 145 with model NeuralProphet in generation 0 of 0\n",
            "Template Eval Error: ModuleNotFoundError(\"No module named 'neuralprophet'\") in model 145 in generation 0: NeuralProphet\n",
            "Model Number: 146 with model KalmanStateSpace in generation 0 of 0\n",
            "Model Number: 147 with model GLM in generation 0 of 0\n",
            "Model Number: 148 with model KalmanStateSpace in generation 0 of 0\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/families/links.py:198: RuntimeWarning: overflow encountered in exp\n",
            "  t = np.exp(-z)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Number: 149 with model GLS in generation 0 of 0\n",
            "Model Number: 150 with model Theta in generation 0 of 0\n",
            "Model Number: 151 with model ETS in generation 0 of 0\n",
            "ETS error ValueError('endog must be strictly positive when usingmultiplicative trend or seasonal components.')\n",
            "ETS failed on value with ValueError('endog must be strictly positive when usingmultiplicative trend or seasonal components.')\n",
            "Model Number: 152 with model GLM in generation 0 of 0\n",
            "Template Eval Error: ValueError('The first guess on the deviance function returned a nan.  This could be a boundary  problem and should be reported.') in model 152 in generation 0: GLM\n",
            "TotalRuntime missing in 1!\n",
            "Validation Round: 1\n",
            "Model Number: 1 of 23 with model AverageValueNaive for Validation 1\n",
            "📈 1 - AverageValueNaive with avg smape 43.74: \n",
            "Model Number: 2 of 23 with model UnivariateMotif for Validation 1\n",
            "📈 2 - UnivariateMotif with avg smape 43.1: \n",
            "Model Number: 3 of 23 with model UnobservedComponents for Validation 1\n",
            "3 - UnobservedComponents with avg smape 50.77: \n",
            "Model Number: 4 of 23 with model Theta for Validation 1\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/generalized_linear_model.py:307: DomainWarning: The InversePower link function does not respect the domain of the Gamma family.\n",
            "  warnings.warn((f\"The {type(family.link).__name__} link function \"\n",
            "/usr/local/lib/python3.10/dist-packages/statsmodels/genmod/families/family.py:188: RuntimeWarning: invalid value encountered in divide\n",
            "  return np.sum(resid_dev * freq_weights * var_weights / scale)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4 - Theta with avg smape 44.99: \n",
            "Model Number: 5 of 23 with model ARIMA for Validation 1\n",
            "5 - ARIMA with avg smape 59.38: \n",
            "Model Number: 6 of 23 with model UnivariateMotif for Validation 1\n",
            "📈 6 - UnivariateMotif with avg smape 40.69: \n",
            "Model Number: 7 of 23 with model GLS for Validation 1\n",
            "7 - GLS with avg smape 45.14: \n",
            "Model Number: 8 of 23 with model UnivariateMotif for Validation 1\n",
            "8 - UnivariateMotif with avg smape 62.69: \n",
            "Model Number: 9 of 23 with model SeasonalNaive for Validation 1\n",
            "9 - SeasonalNaive with avg smape 57.78: \n",
            "Model Number: 10 of 23 with model Theta for Validation 1\n",
            "10 - Theta with avg smape 75.25: \n",
            "Model Number: 11 of 23 with model LastValueNaive for Validation 1\n",
            "11 - LastValueNaive with avg smape 57.57: \n",
            "Model Number: 12 of 23 with model ARIMA for Validation 1\n",
            "12 - ARIMA with avg smape 65.91: \n",
            "Model Number: 13 of 23 with model SeasonalNaive for Validation 1\n",
            "13 - SeasonalNaive with avg smape 50.77: \n",
            "Model Number: 14 of 23 with model LastValueNaive for Validation 1\n",
            "14 - LastValueNaive with avg smape 50.77: \n",
            "Model Number: 15 of 23 with model ConstantNaive for Validation 1\n",
            "15 - ConstantNaive with avg smape 50.77: \n",
            "Model Number: 16 of 23 with model FBProphet for Validation 1\n",
            "16 - FBProphet with avg smape 50.77: \n",
            "Model Number: 17 of 23 with model Theta for Validation 1\n",
            "📈 17 - Theta with avg smape 40.15: \n",
            "Model Number: 18 of 23 with model UnobservedComponents for Validation 1\n",
            "18 - UnobservedComponents with avg smape 50.4: \n",
            "Model Number: 19 of 23 with model DatepartRegression for Validation 1\n",
            "19 - DatepartRegression with avg smape 57.25: \n",
            "Model Number: 20 of 23 with model ConstantNaive for Validation 1\n",
            "20 - ConstantNaive with avg smape 51.9: \n",
            "Model Number: 21 of 23 with model ARIMA for Validation 1\n",
            "21 - ARIMA with avg smape 50.91: \n",
            "Model Number: 22 of 23 with model DatepartRegression for Validation 1\n",
            "22 - DatepartRegression with avg smape 53.73: \n",
            "Model Number: 23 of 23 with model ETS for Validation 1\n",
            "23 - ETS with avg smape 48.51: \n",
            "TotalRuntime missing in 2!\n",
            "Validation Round: 1\n",
            "TotalRuntime missing in 0!\n"
          ]
        }
      ],
      "source": [
        "from autots import AutoTS\n",
        "\n",
        "\n",
        "# 创建 AutoTS 模型\n",
        "model = AutoTS(forecast_length=10,\n",
        "    frequency='D',\n",
        "    prediction_interval=0.95,\n",
        "    ensemble=None,\n",
        "    max_generations=0,\n",
        "    model_list = 'univariate',\n",
        "    num_validations=1)\n",
        "\n",
        "# 训练模型\n",
        "model = model.fit(df['value'])\n",
        "# 进行时间序列预测\n",
        "prediction = model.predict(forecast_length=15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "XvsmR6mGVJLt",
        "outputId": "e20f95f5-1de1-42d1-eee8-5dd59f4a2c6d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dict_keys(['all', 'default', 'fast', 'superfast', 'parallel', 'fast_parallel', 'fast_parallel_no_arima', 'probabilistic', 'multivariate', 'univariate', 'no_params', 'recombination_approved', 'no_shared', 'no_shared_fast', 'experimental', 'slow', 'gpu', 'regressor', 'best', 'motifs', 'all_result_path', 'regressions', 'all_pragmatic', 'update_fit'])\n"
          ]
        }
      ],
      "source": [
        "from autots.models.model_list import model_lists\n",
        "print(model_lists.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "BB1sKBN3rjlt",
        "outputId": "5acb414e-dafc-43fd-a32d-98f06719bb8c"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Model</th>\n",
              "      <th>ModelParameters</th>\n",
              "      <th>TransformationParameters</th>\n",
              "      <th>Ensemble</th>\n",
              "      <th>Runs</th>\n",
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              "      <th>made</th>\n",
              "      <th>...</th>\n",
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              "      <th>containment_weighted</th>\n",
              "      <th>contour_weighted</th>\n",
              "      <th>TotalRuntimeSeconds</th>\n",
              "      <th>Score</th>\n",
              "      <th>lowest_series_mape</th>\n",
              "      <th>lowest_series_mape_name</th>\n",
              "      <th>highest_series_mape</th>\n",
              "      <th>highest_series_mape_name</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>008b083605243e6109128f6d0d270ca0</td>\n",
              "      <td>Theta</td>\n",
              "      <td>{\"deseasonalize\": true, \"difference\": false, \"...</td>\n",
              "      <td>{\"fillna\": \"mean\", \"transformations\": {\"0\": \"D...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>44.741883</td>\n",
              "      <td>0.269768</td>\n",
              "      <td>0.337115</td>\n",
              "      <td>0.101666</td>\n",
              "      <td>...</td>\n",
              "      <td>0.341628</td>\n",
              "      <td>1.901387</td>\n",
              "      <td>0.95</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.30475</td>\n",
              "      <td>26.064213</td>\n",
              "      <td>16.742963</td>\n",
              "      <td>value</td>\n",
              "      <td>16.742963</td>\n",
              "      <td>value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0181278de69621582f7c03c262606646</td>\n",
              "      <td>ETS</td>\n",
              "      <td>{\"damped_trend\": false, \"trend\": null, \"season...</td>\n",
              "      <td>{\"fillna\": \"zero\", \"transformations\": {\"0\": \"M...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>51.350335</td>\n",
              "      <td>0.238211</td>\n",
              "      <td>0.313508</td>\n",
              "      <td>0.125364</td>\n",
              "      <td>...</td>\n",
              "      <td>0.156557</td>\n",
              "      <td>1.922621</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.05760</td>\n",
              "      <td>58.446656</td>\n",
              "      <td>12.621983</td>\n",
              "      <td>value</td>\n",
              "      <td>12.621983</td>\n",
              "      <td>value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>033c18796b16cbe9a2fab02bdc998ec4</td>\n",
              "      <td>DatepartRegression</td>\n",
              "      <td>{\"regression_model\": {\"model\": \"ExtraTrees\", \"...</td>\n",
              "      <td>{\"fillna\": \"zero\", \"transformations\": {\"0\": \"M...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>50.285733</td>\n",
              "      <td>0.237039</td>\n",
              "      <td>0.316446</td>\n",
              "      <td>0.113460</td>\n",
              "      <td>...</td>\n",
              "      <td>0.249881</td>\n",
              "      <td>1.900795</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.14270</td>\n",
              "      <td>58.073922</td>\n",
              "      <td>13.620883</td>\n",
              "      <td>value</td>\n",
              "      <td>13.620883</td>\n",
              "      <td>value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>03711b5ef9552d302bc7ebdb41f3e46b</td>\n",
              "      <td>GLM</td>\n",
              "      <td>{\"family\": \"Poisson\", \"constant\": false, \"regr...</td>\n",
              "      <td>{\"fillna\": \"rolling_mean\", \"transformations\": ...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>125.729076</td>\n",
              "      <td>2.743585</td>\n",
              "      <td>3.851451</td>\n",
              "      <td>1.420617</td>\n",
              "      <td>...</td>\n",
              "      <td>8.211880</td>\n",
              "      <td>0.144020</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.03530</td>\n",
              "      <td>157.422220</td>\n",
              "      <td>143.272040</td>\n",
              "      <td>value</td>\n",
              "      <td>143.272040</td>\n",
              "      <td>value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>06a3779e9059c2e7a6d07f45e32ed223</td>\n",
              "      <td>GLS</td>\n",
              "      <td>{}</td>\n",
              "      <td>{\"fillna\": \"ffill\", \"transformations\": {\"0\": \"...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>125.767519</td>\n",
              "      <td>2.744759</td>\n",
              "      <td>3.851194</td>\n",
              "      <td>1.420679</td>\n",
              "      <td>...</td>\n",
              "      <td>8.217716</td>\n",
              "      <td>0.144188</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.05510</td>\n",
              "      <td>157.417771</td>\n",
              "      <td>143.333362</td>\n",
              "      <td>value</td>\n",
              "      <td>143.333362</td>\n",
              "      <td>value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>f769a3d19fcf3cf947e74df5aea34d76</td>\n",
              "      <td>Theta</td>\n",
              "      <td>{\"deseasonalize\": true, \"difference\": false, \"...</td>\n",
              "      <td>{\"fillna\": \"SeasonalityMotifImputer\", \"transfo...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>43.837021</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.263821</td>\n",
              "      <td>0.101808</td>\n",
              "      <td>...</td>\n",
              "      <td>0.213724</td>\n",
              "      <td>1.899479</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.15905</td>\n",
              "      <td>19.079992</td>\n",
              "      <td>11.472115</td>\n",
              "      <td>value</td>\n",
              "      <td>11.472115</td>\n",
              "      <td>value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93</th>\n",
              "      <td>f8c672541074641aba060b847c5a91bd</td>\n",
              "      <td>ETS</td>\n",
              "      <td>{\"damped_trend\": false, \"trend\": \"additive\", \"...</td>\n",
              "      <td>{\"fillna\": \"mean\", \"transformations\": {\"0\": \"C...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>75.348923</td>\n",
              "      <td>2.051888</td>\n",
              "      <td>4.206801</td>\n",
              "      <td>1.377169</td>\n",
              "      <td>...</td>\n",
              "      <td>6.354262</td>\n",
              "      <td>0.014943</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.06840</td>\n",
              "      <td>115.619799</td>\n",
              "      <td>107.151127</td>\n",
              "      <td>value</td>\n",
              "      <td>107.151127</td>\n",
              "      <td>value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>fb47a7b5b93137f6c5654149a9de0bf6</td>\n",
              "      <td>SeasonalNaive</td>\n",
              "      <td>{\"method\": \"median\", \"lag_1\": 12, \"lag_2\": 2}</td>\n",
              "      <td>{\"fillna\": \"ffill\", \"transformations\": {\"0\": \"...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>91.760620</td>\n",
              "      <td>2.172844</td>\n",
              "      <td>4.256151</td>\n",
              "      <td>1.458114</td>\n",
              "      <td>...</td>\n",
              "      <td>6.515770</td>\n",
              "      <td>0.411598</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.05840</td>\n",
              "      <td>187.836319</td>\n",
              "      <td>113.467515</td>\n",
              "      <td>value</td>\n",
              "      <td>113.467515</td>\n",
              "      <td>value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>fbdc2b938411b5d03c7bc2fd06b0ac44</td>\n",
              "      <td>UnobservedComponents</td>\n",
              "      <td>{\"level\": true, \"maxiter\": 100, \"cov_type\": \"o...</td>\n",
              "      <td>{\"fillna\": \"fake_date\", \"transformations\": {\"0...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>84.277379</td>\n",
              "      <td>2.109662</td>\n",
              "      <td>4.126213</td>\n",
              "      <td>1.385461</td>\n",
              "      <td>...</td>\n",
              "      <td>6.409739</td>\n",
              "      <td>0.043273</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.23900</td>\n",
              "      <td>407.729905</td>\n",
              "      <td>110.168119</td>\n",
              "      <td>value</td>\n",
              "      <td>110.168119</td>\n",
              "      <td>value</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>fe31d2efd41a9859bf1504ed5828a371</td>\n",
              "      <td>LastValueNaive</td>\n",
              "      <td>{}</td>\n",
              "      <td>{\"fillna\": \"time\", \"transformations\": {\"0\": \"D...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>159.218206</td>\n",
              "      <td>3.012825</td>\n",
              "      <td>3.311006</td>\n",
              "      <td>0.202173</td>\n",
              "      <td>...</td>\n",
              "      <td>8.622920</td>\n",
              "      <td>2.052283</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.04170</td>\n",
              "      <td>122.276507</td>\n",
              "      <td>157.331970</td>\n",
              "      <td>value</td>\n",
              "      <td>157.331970</td>\n",
              "      <td>value</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>97 rows × 48 columns</p>\n",
              "</div>\n",
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              "\n",
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              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        element.innerHTML = '';\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "  .colab-df-spinner {\n",
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              "  @keyframes spin {\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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            "text/plain": [
              "                                  ID                 Model  \\\n",
              "0   008b083605243e6109128f6d0d270ca0                 Theta   \n",
              "1   0181278de69621582f7c03c262606646                   ETS   \n",
              "2   033c18796b16cbe9a2fab02bdc998ec4    DatepartRegression   \n",
              "3   03711b5ef9552d302bc7ebdb41f3e46b                   GLM   \n",
              "4   06a3779e9059c2e7a6d07f45e32ed223                   GLS   \n",
              "..                               ...                   ...   \n",
              "92  f769a3d19fcf3cf947e74df5aea34d76                 Theta   \n",
              "93  f8c672541074641aba060b847c5a91bd                   ETS   \n",
              "94  fb47a7b5b93137f6c5654149a9de0bf6         SeasonalNaive   \n",
              "95  fbdc2b938411b5d03c7bc2fd06b0ac44  UnobservedComponents   \n",
              "96  fe31d2efd41a9859bf1504ed5828a371        LastValueNaive   \n",
              "\n",
              "                                      ModelParameters  \\\n",
              "0   {\"deseasonalize\": true, \"difference\": false, \"...   \n",
              "1   {\"damped_trend\": false, \"trend\": null, \"season...   \n",
              "2   {\"regression_model\": {\"model\": \"ExtraTrees\", \"...   \n",
              "3   {\"family\": \"Poisson\", \"constant\": false, \"regr...   \n",
              "4                                                  {}   \n",
              "..                                                ...   \n",
              "92  {\"deseasonalize\": true, \"difference\": false, \"...   \n",
              "93  {\"damped_trend\": false, \"trend\": \"additive\", \"...   \n",
              "94      {\"method\": \"median\", \"lag_1\": 12, \"lag_2\": 2}   \n",
              "95  {\"level\": true, \"maxiter\": 100, \"cov_type\": \"o...   \n",
              "96                                                 {}   \n",
              "\n",
              "                             TransformationParameters  Ensemble  Runs  \\\n",
              "0   {\"fillna\": \"mean\", \"transformations\": {\"0\": \"D...         0     2   \n",
              "1   {\"fillna\": \"zero\", \"transformations\": {\"0\": \"M...         0     2   \n",
              "2   {\"fillna\": \"zero\", \"transformations\": {\"0\": \"M...         0     2   \n",
              "3   {\"fillna\": \"rolling_mean\", \"transformations\": ...         0     1   \n",
              "4   {\"fillna\": \"ffill\", \"transformations\": {\"0\": \"...         0     1   \n",
              "..                                                ...       ...   ...   \n",
              "92  {\"fillna\": \"SeasonalityMotifImputer\", \"transfo...         0     2   \n",
              "93  {\"fillna\": \"mean\", \"transformations\": {\"0\": \"C...         0     1   \n",
              "94  {\"fillna\": \"ffill\", \"transformations\": {\"0\": \"...         0     1   \n",
              "95  {\"fillna\": \"fake_date\", \"transformations\": {\"0...         0     1   \n",
              "96  {\"fillna\": \"time\", \"transformations\": {\"0\": \"D...         0     1   \n",
              "\n",
              "         smape       mae      rmse      made  ...  uwmse_weighted  \\\n",
              "0    44.741883  0.269768  0.337115  0.101666  ...        0.341628   \n",
              "1    51.350335  0.238211  0.313508  0.125364  ...        0.156557   \n",
              "2    50.285733  0.237039  0.316446  0.113460  ...        0.249881   \n",
              "3   125.729076  2.743585  3.851451  1.420617  ...        8.211880   \n",
              "4   125.767519  2.744759  3.851194  1.420679  ...        8.217716   \n",
              "..         ...       ...       ...       ...  ...             ...   \n",
              "92   43.837021  0.208624  0.263821  0.101808  ...        0.213724   \n",
              "93   75.348923  2.051888  4.206801  1.377169  ...        6.354262   \n",
              "94   91.760620  2.172844  4.256151  1.458114  ...        6.515770   \n",
              "95   84.277379  2.109662  4.126213  1.385461  ...        6.409739   \n",
              "96  159.218206  3.012825  3.311006  0.202173  ...        8.622920   \n",
              "\n",
              "    smoothness_weighted  containment_weighted  contour_weighted  \\\n",
              "0              1.901387                  0.95               0.6   \n",
              "1              1.922621                  1.00               0.5   \n",
              "2              1.900795                  1.00               0.6   \n",
              "3              0.144020                  0.80               0.4   \n",
              "4              0.144188                  0.80               0.4   \n",
              "..                  ...                   ...               ...   \n",
              "92             1.899479                  0.65               0.6   \n",
              "93             0.014943                  1.00               0.4   \n",
              "94             0.411598                  0.50               0.1   \n",
              "95             0.043273                  1.00               0.5   \n",
              "96             2.052283                  1.00               0.8   \n",
              "\n",
              "    TotalRuntimeSeconds       Score  lowest_series_mape  \\\n",
              "0               0.30475   26.064213           16.742963   \n",
              "1               0.05760   58.446656           12.621983   \n",
              "2               0.14270   58.073922           13.620883   \n",
              "3               0.03530  157.422220          143.272040   \n",
              "4               0.05510  157.417771          143.333362   \n",
              "..                  ...         ...                 ...   \n",
              "92              0.15905   19.079992           11.472115   \n",
              "93              0.06840  115.619799          107.151127   \n",
              "94              0.05840  187.836319          113.467515   \n",
              "95              0.23900  407.729905          110.168119   \n",
              "96              0.04170  122.276507          157.331970   \n",
              "\n",
              "    lowest_series_mape_name  highest_series_mape  highest_series_mape_name  \n",
              "0                     value            16.742963                     value  \n",
              "1                     value            12.621983                     value  \n",
              "2                     value            13.620883                     value  \n",
              "3                     value           143.272040                     value  \n",
              "4                     value           143.333362                     value  \n",
              "..                      ...                  ...                       ...  \n",
              "92                    value            11.472115                     value  \n",
              "93                    value           107.151127                     value  \n",
              "94                    value           113.467515                     value  \n",
              "95                    value           110.168119                     value  \n",
              "96                    value           157.331970                     value  \n",
              "\n",
              "[97 rows x 48 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# accuracy of all tried model results\n",
        "model_results = model.results()\n",
        "# and aggregated from cross validation\n",
        "validation_results = model.results(\"validation\")\n",
        "validation_results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "wTDI7pN9mGTr",
        "outputId": "71301225-aaff-4c4b-d9af-1fd746758baf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Initiated AutoTS object with best model: \n",
            "AverageValueNaive\n",
            "{'fillna': 'rolling_mean_24', 'transformations': {'0': 'SeasonalDifference', '1': 'Round', '2': 'Detrend'}, 'transformation_params': {'0': {'lag_1': 7, 'method': 'Mean'}, '1': {'model': 'middle', 'decimals': 2, 'on_transform': True, 'on_inverse': False}, '2': {'model': 'GLS'}}}\n",
            "{'method': 'Mean', 'window': None}\n",
            "Validation: 0, 1\n",
            "SMAPE: 44.2417717051393, 43.736855738250696\n",
            "MAE: 0.18444146610144338, 0.212913989779809\n",
            "SPL: 0.015923738020116773, 0.01996588882917803\n"
          ]
        }
      ],
      "source": [
        "# Print the details of the best model\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "3O7QbmdLSmbk",
        "outputId": "be9f58bb-f31f-449a-e758-83c791664c6f"
      },
      "outputs": [
        {
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              "    <tr>\n",
              "      <th>2019-04-11</th>\n",
              "      <td>0.418158</td>\n",
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              "</div>\n",
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            ],
            "text/plain": [
              "                value\n",
              "2019-04-11   0.418158\n",
              "2019-04-12   0.543886\n",
              "2019-04-13   0.568646\n",
              "2019-04-14   0.433800\n",
              "2019-04-15   0.537693\n",
              "2019-04-16  10.000301\n",
              "2019-04-17   0.432655\n",
              "2019-04-18   0.418158\n",
              "2019-04-19   0.543886\n",
              "2019-04-20   0.568646\n",
              "2019-04-21   0.433800\n",
              "2019-04-22   0.537693\n",
              "2019-04-23  10.000301\n",
              "2019-04-24   0.432655\n",
              "2019-04-25   0.418158"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "prediction.forecast"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "FXvRnbIizC-e",
        "outputId": "88b9a087-4ed5-4c25-ff0d-8e00283667c8"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7afdf7dd42b0>]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 2000x300 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "fig,ax = plt.subplots(figsize=(20,3))\n",
        "ax.plot(df['value'])\n",
        "ax.plot(prediction.forecast)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ISma4azerLKC"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}