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Section: New Results

Day-ahead Time Series Forecasting: Application to Capacity Planning

Participants: C. Leverger, V. Lemaire, S. Malinowski, T. Guyet, L. Rozé

In the context of capacity planning, forecasting the evolution of server usage enables companies to better manage their computational resources. The work in [12] addresses this problem by collecting key indicator time series. The article proposes a method to forecast the evolution of server usage one day-ahead. The 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).