Toward a Framework for Seasonal Time Series Forecasting Using Clustering - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

Toward a Framework for Seasonal Time Series Forecasting Using Clustering

(1) , (2) , (1) , (3) , (4) , (5)
1
2
3
4
5

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.
Fichier principal
Vignette du fichier
IDEAL2019.pdf (1.59 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02371221 , version 1 (20-11-2019)

Identifiers

Cite

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⟩
194 View
345 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More