Abstract
Time-series forecasting is an important research and application area. Much effort has been devoted over the past decades to develop and improve the time series forecasting models based on statistical and machine learning techniques. Forecast combination is a well-established and well-tested approach for improving forecasting accuracy. Many time series may contain some structural breaks that may affect the performance of forecasting due to the varying nature of the dynamics with time. In this study we investigate the performance of using forecast combination in handling these breaks, and in mitigating the effects of discontinuities in time series.
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Azmy, W.M., Atiya, A.F., El-Shishiny, H. (2010). Forecast Combination Strategies for Handling Structural Breaks for Time Series Forecasting. In: El Gayar, N., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2010. Lecture Notes in Computer Science, vol 5997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12127-2_25
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DOI: https://doi.org/10.1007/978-3-642-12127-2_25
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