Tab2vox: CNN-Based Multivariate Multilevel Demand Forecasting Framework by Tabular-To-Voxel Image Conversion

TL;DR

This paper introduces Tab2vox, a neural architecture that converts high-dimensional tabular data into 3D voxel images for improved demand forecasting using CNNs, outperforming traditional methods.

Contribution

The study presents a novel Tab2vox framework that transforms tabular data into 3D voxel images, enabling CNN-based demand forecasting with superior accuracy.

Findings

3D CNN models outperform traditional time series methods.

Tab2vox effectively captures multi-level contextual information.

The approach improves demand forecast accuracy in volatile markets.

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.