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Monash Time Series Forecasting Archive

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arxiv 2105.06643 v1 pith:L6PAULA3 submitted 2021-05-14 cs.LG stat.ML

Rakshitha Godahewa , Christoph Bergmeir , Geoffrey I. Webb , Rob J. Hyndman , Pablo Montero-Manso This is my paper · ORCID
classification cs.LGstat.ML
keywords seriesforecastingtimedatasetsarchiveacrossalgorithmsavailable
verification ladder T0 review T1 audit T2 compute T3 formal
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Many businesses and industries nowadays rely on large quantities of time series data making time series forecasting an important research area. Global forecasting models that are trained across sets of time series have shown a huge potential in providing accurate forecasts compared with the traditional univariate forecasting models that work on isolated series. However, there are currently no comprehensive time series archives for forecasting that contain datasets of time series from similar sources available for the research community to evaluate the performance of new global forecasting algorithms over a wide variety of datasets. In this paper, we present such a comprehensive time series forecasting archive containing 20 publicly available time series datasets from varied domains, with different characteristics in terms of frequency, series lengths, and inclusion of missing values. We also characterise the datasets, and identify similarities and differences among them, by conducting a feature analysis. Furthermore, we present the performance of a set of standard baseline forecasting methods over all datasets across eight error metrics, for the benefit of researchers using the archive to benchmark their forecasting algorithms.

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Cited by 17 Pith papers

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