Abstract
In this paper, a technique for selecting individual methods in a combined time series forecasting model is described. A neural network at the input of which a vector of time series metrics is proposed. The metrics corresponds to significant characteristics of the time series. The values of the metrics are easily computed. The neural network calculates the estimated prediction error for each model from the base set of the combined model. The proposed selection method is most effective for short time series and when the base set contains a lot of complex prediction methods. The developed system was tested on the time series from the CIF 2015-2016 competitions. According to the result of the experiment, the application of the developed system allowed to reduce the average forecast error from 13 to 9 percent.
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This work was supported by the Russian Foundation for Basic Research. Projects No. 18-47-730035, No. 19-07-00999 and 18-47-732007.
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Yashin, D., Moshkina, I., Moshkin, V. (2020). An Approach to the Selection of a Time Series Analysis Method Using a Neural Network. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12249. Springer, Cham. https://doi.org/10.1007/978-3-030-58799-4_50
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