Abstract
Because of seasonality of demand and periodicity of replenishments, inventory dynamics can be highly self-similar. This paper demonstrates that such metrics of fractal analysis as the Hurst exponent, correlation dimension and sample entropy indicate predictability of inventory dynamics by LSTM recurrent neural networks. From business point of view, this finding is useful, because one can spot time series with large self-similarity metrics prior to building the model saving time and computational budget as the result.
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Jackson, I., Grakovski, A. (2020). Combining LSTM Artificial Recurrent Neural Networks and Fractal Analysis for Inventory Dynamics Prediction. In: Kabashkin, I., Yatskiv, I., Prentkovskis, O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2019. Lecture Notes in Networks and Systems, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-030-44610-9_3
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DOI: https://doi.org/10.1007/978-3-030-44610-9_3
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