Abstract
Seasonal behaviours are widely encountered in various applications. For instance, requests on web servers are highly influenced by our daily activities. Seasonal forecasting consists in forecasting the whole next season for a given seasonal time series. It may help a service provider to provision correctly the potentially required resources, avoiding critical situations of over- or under provision. In this article, we propose a generic framework to make seasonal time series forecasting. The framework combines machine learning techniques (1) to identify the typical seasons and (2) to forecast the likelihood of having a season type in one season ahead. We study this framework by comparing the mean squared errors of forecasts for various settings and various datasets. The best setting is then compared to state-of-the-art time series forecasting methods. We show that it is competitive with them.
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Notes
- 1.
In practice, it is not a problem, as most time series could be easily decomposed and detrended. Trend components can then be re-applied on the forecasted values.
- 2.
For reproducible research, code and data are available online https://github.com/ColinLeverger/IDEAL2019-coclustering-forecasts.
- 3.
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Leverger, C., Malinowski, S., Guyet, T., Lemaire, V., Bondu, A., Termier, A. (2019). Toward a Framework for Seasonal Time Series Forecasting Using Clustering. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11871. Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_36
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