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• W4296744019 abstract "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 and use adjacent voxel sets to integrate multi-dimensional important information and learn important features. On the other hand, although the demand-forecasting model has been improved, the input data is still limited in its tabular form and is not suitable for CNN modeling. In this study, we propose a Tab2vox neural architecture search (NAS) model as a method to convert a high-dimensional tabular sam-ple into a well-formed 3D voxel image and use it in a 3D CNN network. For each image repre-sentation, the 3D CNN forecasting model proposed from the Tab2vox framework showed supe-rior performance, compared to the existing time series and machine learning techniques using tabular data, and the latest image transformation studies." @default.
• W4296744019 created "2022-09-23" @default.
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• W4296744019 date "2022-09-19" @default.
• W4296744019 modified "2023-09-25" @default.
• W4296744019 title "Tab2vox: CNN-Based Multivariate Multilevel Demand Forecasting Framework by Tabular-To-Voxel Image Conversion" @default.
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• W4296744019 doi "https://doi.org/10.3390/su141811745" @default.
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