  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.
