TSForecasting

This repository contains the implementations related to the experiments of a set of publicly available datasets that are used in the time series forecasting research space.

The benchmark datasets are available at: https://zenodo.org/communities/forecasting. For more details, please refer to our website: https://forecastingdata.org/ and paper: https://arxiv.org/abs/2105.06643.

All datasets contain univariate time series and they are availble in a new format that we name as .tsf, pioneered by the sktime .ts format. The data can be loaded into the R environment in tsibble format [1] by following the example in "utils/data_loader.R". It uses a similar approach to the arff file loading method in R foreign package [2]. The data can be loaded into the Python environment as a Pandas dataframe by following the example in "utils/data_loader.py". Download the .tsf files as required from our Zenodo dataset repository and put them into "tsf_data" folder.

The fixed horizon, rolling origin and feature calculation related experiments are there in the "experiments" folder. Please see the examples in the corresponding R scripts in the "experiments" folder for more details. Makesure to create a folder named "results" in the parent level and sub-folders as necessary before running the experiments. The outputs of the experiments will be stored into the sub-folders within the "results" folder as mentioned follows:

Sub-folder Name	Stored Output
rolling_origin_forecasts	rolling origin forecasts
rolling_origin_errors	rolling origin errors
rolling_origin_execution_times	rolling origin execution times
fixed_horizon_forecasts	fixed horizon forecasts
fixed_horizon_errors	fixed horizon errors
fixed_horizon_execution_times	fixed horizon execution times
tsfeatures	tsfeatures
catch22_features	catch22 features
lambdas	boxcox lambdas

Citing Our Work

When using this repository, please cite:

@misc{godahewa2021monash,
    author="Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoffrey I. and Hyndman, Rob J. and Montero-Manso, Pablo",
    title="Monash Time Series Forecasting Archive",
    howpublished ="\url{https://arxiv.org/abs/2105.06643}",
    year="2021"
}

References

[1] Wang, E., Cook, D., Hyndman, R. J. (2020). A new tidy data structure to support exploration and modeling of temporal data. Journal of Computational and Graphical Statistics. doi:10.1080/10618600.2019.1695624.

[2] R Core Team (2018). foreign: Read Data Stored by 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka', 'dBase', .... R package version 0.8-71. https://CRAN.R-project.org/package=foreign