Improving forecasting by estimating time series structural components across multiple frequencies

Nikolaos Kourentzes, Fotios Petropoulos, J.R Trapero Arenas

Research output: Contribution to journalArticlepeer-review

143 Citations (SciVal)
283 Downloads (Pure)


Identifying the most appropriate time series model to achieve a good forecasting accuracy is a challenging task. We propose a novel algorithm that aims to mitigate the importance of model selection, while increasing the accuracy. Multiple time series are constructed from the original time series, using temporal aggregation. These derivative series highlight different aspects of the original data, as temporal aggregation helps in strengthening or attenuating the signals of different time series components. In each series, the appropriate exponential smoothing method is fitted and its respective time series components are forecast. Subsequently, the time series components from each aggregation level are combined, then used to construct the final forecast. This approach achieves a better estimation of the different time series components, through temporal aggregation, and reduces the importance of model selection through forecast combination. An empirical evaluation of the proposed framework demonstrates significant improvements in forecasting accuracy, especially for long-term forecasts.
Original languageEnglish
Pages (from-to)291-302
Number of pages12
JournalInternational Journal of Forecasting
Issue number2
Early online date28 Dec 2013
Publication statusPublished - 1 Apr 2014


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