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docs for forecasting task #443

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merged 7 commits into from
Jul 6, 2022
Merged

docs for forecasting task #443

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51aa88f
docs for forecasting task
dengdifan Jun 28, 2022
6a969f1
avoid directly import extra dependencies
dengdifan Jun 28, 2022
2145b68
Update docs/dev.rst
dengdifan Jun 28, 2022
2b8f6d1
make ForecastingDependenciesNotInstalledError a str message
dengdifan Jun 28, 2022
3db6626
make ForecastingDependenciesNotInstalledError a str message
dengdifan Jun 28, 2022
12ba700
update readme and examples
dengdifan Jun 29, 2022
be290e1
add explanation for univariant models in example
dengdifan Jun 29, 2022
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update readme and examples
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@dengdifan
dengdifan committed Jun 29, 2022
commit 12ba700f120f755a96a9b5b5a17e3c3879d04b51
9 changes: 6 additions & 3 deletions README.md
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Expand Up @@ -142,8 +142,11 @@ targets, features = load_longley()
# define the forecasting horizon
forecasting_horizon = 3

# each series represent an element in the List
# we take the last forecasting_horizon as test targets. The itme before that as training targets
# Dataset optimized by APT-TS can be a list of np.ndarray/ pd.DataFrame where each series represents an element in the
# list, or a single pd.DataFrame that records the series
# index information: to which series the timestep belongs? This id can be stored as the DataFrame's index or a separate
# column
# Within each series, we take the last forecasting_horizon as test targets. The items before that as training targets
# Normally the value to be forecasted should follow the training sets
y_train = [targets[: -forecasting_horizon]]
y_test = [targets[-forecasting_horizon:]]
Expand All @@ -169,7 +172,7 @@ api.search(
X_test=X_test,
optimize_metric='mean_MAPE_forecasting',
n_prediction_steps=forecasting_horizon,
memory_limit=16 * 1024, # Currently, forecasting models need much more memories than it actually requires
memory_limit=16 * 1024, # Currently, forecasting models use much more memories
freq=freq,
start_times=start_times,
func_eval_time_limit_secs=50,
Expand Down
9 changes: 6 additions & 3 deletions examples/20_basics/example_time_series_forecasting.py
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Expand Up @@ -25,8 +25,11 @@

forecasting_horizon = 3

# each series represent an element in the List
# we take the last forecasting_horizon as test targets. The itme before that as training targets
# Dataset optimized by APT-TS can be a list of np.ndarray/ pd.DataFrame where each series represents an element in the
# list, or a single pd.DataFrame that records the series
# index information: to which series the timestep belongs? This id can be stored as the DataFrame's index or a separate
# column
# Within each series, we take the last forecasting_horizon as test targets. The items before that as training targets
# Normally the value to be forecasted should follow the training sets
y_train = [targets[: -forecasting_horizon]]
y_test = [targets[-forecasting_horizon:]]
Expand Down Expand Up @@ -57,7 +60,7 @@
X_test=X_test,
optimize_metric='mean_MASE_forecasting',
n_prediction_steps=forecasting_horizon,
memory_limit=16 * 1024, # Currently, forecasting models need much more memories than it actually requires
memory_limit=16 * 1024, # Currently, forecasting models use much more memories
freq=freq,
start_times=start_times,
func_eval_time_limit_secs=50,
Expand Down