Web: http://arxiv.org/abs/2209.10516

Sept. 22, 2022, 1:13 a.m. | Euna Lee, Myungwoo Nam, Hongchul Lee

stat.ML updates on arXiv.org arxiv.org

Since demand is influenced by a wide variety of causes, it is necessary to
decompose the explana-tory variables into different levels, extract their
relationships effectively, and reflect them in the forecast. In particular,
this contextual information can be very useful in demand forecasting with large
demand volatility or intermittent demand patterns. Convolutional neural
networks (CNNs) have been successfully used in many fields where important
information in data is represented by images. CNNs are powerful because they
accept samples as images …

arxiv cnn conversion forecasting framework image tabular voxel

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