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Improving forecasting by estimating time series structural components across multiple frequencies

Kourentzes, Nikolaos, Petropoulos, Fotios and Trapero, Juan R. 2014. Improving forecasting by estimating time series structural components across multiple frequencies. International Journal of Forecasting 30 (2) , pp. 291-302. 10.1016/j.ijforecast.2013.09.006

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Abstract

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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Subjects: H Social Sciences > HF Commerce
H Social Sciences > HG Finance
Additional Information: Pdf uploaded in accordance with publisher's policy at http://www.sherpa.ac.uk/romeo/issn/0169-2070/ (accessed 3.10.14).
Publisher: Elsevier
ISSN: 0169-2070
Last Modified: 06 May 2023 04:27
URI: https://orca.cardiff.ac.uk/id/eprint/62786

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