Day-ahead time series forecasting: application to capacity planning
This work addresses capacity planning for companies managing computational resources, but it appears incremental as it builds on existing time series forecasting techniques.
The paper tackles day-ahead forecasting of informatics server usage for capacity planning by proposing a method that combines clustering and Markov Models, and it shows that this approach outperforms classical methods like AR and Holt-Winters on real datasets.
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).