Toward a Framework for Seasonal Time Series Forecasting Using Clustering

Colin Leverger 1 Simon Malinowski 2 Thomas Guyet 1 Vincent Lemaire 3 Alexis Bondu 4 Alexandre Termier 5
1 LACODAM - Large Scale Collaborative Data Mining
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
2 LinkMedia - Creating and exploiting explicit links between multimedia fragments
Inria Rennes – Bretagne Atlantique , IRISA-D6 - MEDIA ET INTERACTIONS
5 LIG Laboratoire d'Informatique de Grenoble - HADAS
LIG - Laboratoire d'Informatique de Grenoble
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 then.
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Submitted on : Wednesday, November 20, 2019 - 1:31:08 PM
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Colin Leverger, Simon Malinowski, Thomas Guyet, Vincent Lemaire, Alexis Bondu, et al.. Toward a Framework for Seasonal Time Series Forecasting Using Clustering. IDEAL 2019, Nov 2019, Manchester, United Kingdom. pp.328-340, ⟨10.1007/978-3-030-33607-3_36⟩. ⟨hal-02371221⟩

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