Time series clustering using numerical and fuzzy representations

Afanasieva, T. V.; Yarushkina, Nadezhda; Sibirev, Ivan

doi:10.1109/ifsa-scis.2017.8023356

Cited by 5 publications

(1 citation statement)

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“…• Time series clustering using numerical and fuzzy representations (Afanasieva et al, 2017) • An automatic calibration framework applied on a metaheuristic fuzzy model for the CIF competition (Coelho et al, 2016) 2.2.7. Kaggle Web Traffic Dataset This dataset contains 145063 daily time series representing the number of hits or web traffic for a set of Wikipedia pages from 01/07/2015 to 10/09/2017 used by the Kaggle web traffic forecasting competition (Google, 2017).…”

Section: Cif 2016 Datasetmentioning

confidence: 99%

Monash Time Series Forecasting Archive

Godahewa¹,

Bergmeir²,

Webb³

et al. 2021

Preprint

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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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Section: Cif 2016 Datasetmentioning

confidence: 99%