Day-ahead time series forecasting: application to capacity planning

Colin Leverger (1, 2) , Vincent Lemaire (3) , Simon Malinowski (4, 5) , Thomas Guyet (1, 6) , Laurence Rozé (1, 7)

1 LACODAM - Large Scale Collaborative Data Mining
2 Orange Labs [Cesson-Sévigné]
3 Orange Labs [Lannion]
4 UR - Université de Rennes
5 LinkMedia - Creating and exploiting explicit links between multimedia fragments
6 AGROCAMPUS OUEST
7 INSA Rennes - Institut National des Sciences Appliquées - Rennes

In the context of capacity planning, forecasting the evolution of informatics servers usage enables companies to better manage their computational resources. We address this problem by collecting key indicator time series and propose to forecast their evolution a day-ahead. Our method assumes that data is structured by a daily seasonality, but also that there is typical evolution of indicators within a day. Then, it uses the combination of a clustering algorithm and Markov Models to produce day-ahead forecasts. Our experiments on real datasets show that the data satisfies our assumption and that, in the case study, our method outperforms classical approaches (AR, Holt-Winters).

Mots clés: Markov Models; Clustering; Time series; Capacity Planning

Domaines: Intelligence artificielle [cs.AI]

Colin Leverger, Vincent Lemaire, Simon Malinowski, Thomas Guyet, Laurence Rozé. Day-ahead time series forecasting: application to capacity planning. AALTD'18 - 3nd ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, Sep 2018, Dublin, Ireland. ⟨hal-01912002⟩