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Probabilistic forecasting of seasonal time series Combining clustering and classification for forecasting

Abstract : In this article, we propose a framework for seasonal time series probabilistic forecasting. It aims at forecasting (in a probabilistic way) the whole next season of a time series, rather than only the next value. Probabilistic forecasting consists in forecasting a probability distribution function for each future position. The proposed framework is implemented combining several machine learning techniques 1) to identify typical seasons and 2) to forecast a probability distribution of the next season. This framework is evaluated using a wide range of real seasonal time series. On the one side, we intensively study the alternative combinations of the algorithms composing our framework (clustering, classification), and on the other side, we evaluate the framework forecasting accuracy. As demonstrated by our experiences, the proposed framework outperforms competing approaches by achieving lower forecasting errors.
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https://hal.archives-ouvertes.fr/hal-03326626
Contributor : Thomas Guyet Connect in order to contact the contributor
Submitted on : Thursday, August 26, 2021 - 11:47:45 AM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM
Long-term archiving on: : Saturday, November 27, 2021 - 6:21:09 PM

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  • HAL Id : hal-03326626, version 1

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Colin Leverger, Thomas Guyet, Simon Malinowski, Vincent Lemaire, Alexis Bondu, et al.. Probabilistic forecasting of seasonal time series Combining clustering and classification for forecasting. ITISE 2021 - 7th International Conference on Time Series and Forecasting, Jul 2021, Gran Canaria, Spain. pp.1-13. ⟨hal-03326626⟩

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