  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.

Machine Learning;Machine Learning