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Erschienen in: Neural Computing and Applications 2/2014

01.08.2014 | Original Article

A linear hybrid methodology for improving accuracy of time series forecasting

verfasst von: Ratnadip Adhikari, R. K. Agrawal

Erschienen in: Neural Computing and Applications | Ausgabe 2/2014

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Abstract

Modeling and forecasting of time series data are integral parts of many scientific and engineering applications. Increasing precision of the performed forecasts is highly desirable but a difficult task, facing a number of mathematical as well as decision-making challenges. This paper presents a novel approach for linearly combining multiple models in order to improve time series forecasting accuracy. Our approach is based on the assumption that each future observation of a time series is a linear combination of the arithmetic mean and median of the forecasts from all participated models together with a random noise. The proposed ensemble is constructed with five different forecasting models and is tested on six real-world time series. Obtained results demonstrate that the forecasting accuracies are significantly improved through our combination mechanism. A nonparametric statistical analysis is also carried out to show the superior forecasting performances of the proposed ensemble scheme over the individual models as well as a number of other forecast combination techniques.

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Metadaten
Titel
A linear hybrid methodology for improving accuracy of time series forecasting
verfasst von
Ratnadip Adhikari
R. K. Agrawal
Publikationsdatum
01.08.2014
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 2/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-013-1480-1

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