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Toward a Framework for Seasonal Time Series Forecasting Using Clustering

  • Colin LevergerEmail author
  • Simon Malinowski
  • Thomas Guyet
  • Vincent Lemaire
  • Alexis Bondu
  • Alexandre Termier
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11871)

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.

Keywords

Time series Forecasting Time series clustering Naive Bayesian prediction 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Orange LabsRennesFrance
  2. 2.Univ Rennes, Inria, CNRS, IRISARennesFrance
  3. 3.AGROCAMPUS-OUEST/IRISA - UMR 6074RennesFrance

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