A latent information function to extend domain attributes to improve the accuracy of small-data-set forecasting

• Small-data-set forecasting problem is difficult for most manufacturing environments.
• Short-term predictions using new limited data for engineers and managers are more effective and efficient.
• The proposed method, Latent Information function, can analyze data features and extract hidden information for knowledge learning with small data sets.
• The proposed method is considered an appropriate procedure in general to forecast manufacturing outputs based on small samples.

In the current highly competitive manufacturing environment, it is important to have effective and efficient control of manufacturing systems to obtain and maintain competitive advantages. However, developing appropriate forecasting models for such systems can be challenging in their early stages, as the sample sizes are usually very small, and thus there is limited data available for analysis. The technique of virtual sample generation is one way to address this issue, but this method is usually not directly applied to time series data. This research thus develops a Latent Information function to analyze data features and extract hidden information, in order to learn from small data sets considering timing factors. The experimental results obtained using the Synthetic Control Chart Time Series and aluminum price datasets show that the proposed method can significantly improve forecasting accuracy, and thus is considered an appropriate procedure to forecast manufacturing outputs based on small samples.