Electronic Device Demand Forecast Modeling by Instance-Based Domain Adaptation Using Time Series Features

Abstract

In the manufacturing industry, there is a problem that it is difficult to predict with sufficient accuracy for practical use because the amount of training data is small with only the data of the prediction target product. Therefore, in this method, in the extraction of training data from the source domain, the training data of the target domain is extended by performing clustering using time-series features that represent the demand characteristics of electronic devices. We compared our method with conventional methods that do not use domain adaptation, and improved the short-term demand forecast accuracy.