Time Series Forecasting Experiments

In this notebook we will show how to run a simple time series forecasting experiment using the tsml-eval package. Time series forecasting is the task of predicting future values of a time series.

# import numpy as np
# from aeon.datasets import load_airline
# from aeon.forecasting.model_selection import temporal_train_test_split
#
# from tsml_eval.evaluation.storage import load_forecaster_results
# from tsml_eval.experiments import (
#     experiments,
#     get_forecaster_by_name,
#     run_forecasting_experiment,
# )

# y = load_airline()
# train, test = temporal_train_test_split(y)

# # set_regressor can be used to find various regressors by string, but
# # any aeon, tsml or sklearn regressor can be used in the experiments function
# forecaster = get_forecaster_by_name("NaiveForecaster")
#
# # record memory usage every 0.1 seconds, just here for notebook speed
# # does not need to be changed for usage
# experiments.MEMRECORD_INTERVAL = 0.1
#
# run_forecasting_experiment(
#     np.array(train),
#     np.array(test),
#     forecaster,
#     "./generated_results/",
#     dataset_name="Airline",
#     random_seed=0,
# )

A function is also available to load the dataset as well as run an experiment, see load_and_run_forecasting_experiment in tsml_eval.experiments.

Both experiment functions will output a results file in the {results_dir}/{forecaster_name}/Predictions/{dataset_name}/ directory. These files can be loaded individually, or used as a collection in the evaluation module. See the evaluation notebook for more details.

# fr = load_forecaster_results(
#     "./generated_results/NaiveForecaster/Predictions/Airline/testResample0.csv"
# )
# print(fr.predictions)
# print(fr.mean_absolute_percentage_error)

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