Nixtla    

Statistical ⚡️ Forecast

Lightning fast forecasting with statistical and econometric models

StatsForecast offers a collection of widely used univariate time series forecasting models, including exponential smoothing and automatic ARIMA modeling optimized for high performance using numba.

Getting started • Installation

🔥 Features

• Fastest and most accurate auto_arima in Python and R (for the moment...).
• Out of the box implementation of other classical models and benchmarks like exponential smoothing, croston, sesonal naive, random walk with drift and tbs.
• 20x faster than pmdarima.
• 1.5x faster than R.
• 500x faster than Prophet.
• Compiled to high performance machine code through numba.

📖 Why?

Current Python alternatives for statistical models are slow and inaccurate. So we created a library that can be used to forecast in production environments or as benchmarks. StatsForecast includes an extensive battery of models that can efficiently fit thousands of time series.

🔬 Accuracy

We compared accuracy and speed against: pmdarima, Rob Hyndman's forecast package and Facebook's Prophet. We used the Daily, Hourly and Weekly data from the M4 competition.

The following table summarizes the results. As can be seen, our auto_arima is the best model in accuracy (measured by the MASE loss) and time, even compared with the original implementation in R.

dataset	metric	nixtla	pmdarima [1]	auto_arima_r	prophet
M4-Daily	MASE	3.26	3.35	4.46	14.26
M4-Daily	time	1.41	27.61	1.81	514.33
M4-Hourly	MASE	0.92	---	1.02	1.78
M4-Hourly	time	12.92	---	23.95	17.27
M4-Weekly	MASE	2.34	2.47	2.58	7.29
M4-Weekly	time	0.42	2.92	0.22	19.82

[1] The model auto_arima from pmdarima had problems with Hourly data. An issue was opened in their repo.

The following table summarizes the data details.

group	n_series	mean_length	std_length	min_length	max_length
Daily	4,227	2,371	1,756	107	9,933
Hourly	414	901	127	748	1,008
Weekly	359	1,035	707	93	2,610

⏲ Computational efficiency

We measured the computational time against the number of time series. The following graph shows the results. As we can see, the fastest model is our auto_arima.

Nixtla vs Prophet

You can reproduce the results here.

👾 Less code

📖 Documentation

Here is a link to the documentation.

🧬 Getting Started

Example Jupyter Notebook

💻 Installation

PyPI

You can install the released version of StatsForecast from the Python package index with:

pip install statsforecast

(Installing inside a python virtualenvironment or a conda environment is recommended.)

Conda

Also you can install the released version of StatsForecast from conda with:

conda install -c nixtla statsforecast

(Installing inside a python virtualenvironment or a conda environment is recommended.)

Dev Mode

If you want to make some modifications to the code and see the effects in real time (without reinstalling), follow the steps below:

git clone https://github.com/Nixtla/statsforecast.git
cd statsforecast
pip install -e .

🧬 How to use

import numpy as np
import pandas as pd
from IPython.display import display, Markdown

import matplotlib.pyplot as plt
from statsforecast import StatsForecast
from statsforecast.models import seasonal_naive, auto_arima
from statsforecast.utils import AirPassengers

horizon = 12
ap_train = AirPassengers[:-horizon]
ap_test = AirPassengers[-horizon:]

series_train = pd.DataFrame(
    {
        'ds': np.arange(1, ap_train.size + 1),
        'y': ap_train
    },
    index=pd.Index([0] * ap_train.size, name='unique_id')
)

def display_df(df):
    display(Markdown(df.to_markdown()))

fcst = StatsForecast(
    series_train, 
    models=[(auto_arima, 12), (seasonal_naive, 12)], 
    freq='M', 
    n_jobs=1
)
forecasts = fcst.forecast(12)
display_df(forecasts)

unique_id	ds	auto_arima_season_length-12	seasonal_naive_season_length-12
0	133	424.16	360
0	134	407.082	342
0	135	470.861	406
0	136	460.914	396
0	137	484.901	420
0	138	536.904	472
0	139	612.903	548
0	140	623.903	559
0	141	527.903	463
0	142	471.903	407
0	143	426.903	362
0	144	469.903	405

forecasts['y_test'] = ap_test

fig, ax = plt.subplots(1, 1, figsize = (20, 7))
pd.concat([series_train, forecasts]).set_index('ds').plot(ax=ax, linewidth=2)
ax.set_title('AirPassengers Forecast', fontsize=22)
ax.set_ylabel('Monthly Passengers', fontsize=20)
ax.set_xlabel('Timestamp [t]', fontsize=20)
ax.legend(prop={'size': 15})
ax.grid()
for label in (ax.get_xticklabels() + ax.get_yticklabels()):
    label.set_fontsize(20)

🔨 How to contribute

See CONTRIBUTING.md.

📃 References

• The auto_arima model is based (translated) from the R implementation included in the forecast package developed by Rob Hyndman.