TY - JOUR

T1 - Improving forecasting by estimating time series structural components across multiple frequencies

AU - Kourentzes, Nikos

AU - Petropoulos, Fotios

AU - Trapero, Juan Ramon

N1 - The final, definitive version of this article has been published in the Journal, International Journal of Forecasting 30 (2), 2014, © ELSEVIER.

PY - 2014/4

Y1 - 2014/4

N2 - Identifying the appropriate time series model to achieve good forecasting accuracy is a challenging task. We propose a novel algorithm that aims to mitigate the importance of model selection, while increasing accuracy. From the original time series, using temporal aggregation, multiple time series are constructed. 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 forecasted. Subsequently, the time series components from each aggregation level are combined, and then used to construct the final forecast. This approach achieves better estimation of the different time series components, through temporal aggregation, and mitigates the importance of model selection through forecast combination. Empirical evaluation of the proposed framework demonstrates significant improvements in forecasting accuracy, especially for long-term forecasts.

AB - Identifying the appropriate time series model to achieve good forecasting accuracy is a challenging task. We propose a novel algorithm that aims to mitigate the importance of model selection, while increasing accuracy. From the original time series, using temporal aggregation, multiple time series are constructed. 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 forecasted. Subsequently, the time series components from each aggregation level are combined, and then used to construct the final forecast. This approach achieves better estimation of the different time series components, through temporal aggregation, and mitigates the importance of model selection through forecast combination. Empirical evaluation of the proposed framework demonstrates significant improvements in forecasting accuracy, especially for long-term forecasts.

U2 - 10.1016/j.ijforecast.2013.09.006

DO - 10.1016/j.ijforecast.2013.09.006

M3 - Journal article

VL - 30

SP - 291

EP - 302

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 2

ER -