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

Che Jung Chang, Der Chiang Li, Wen Li Dai, Chien Chih Chen

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)343-349
Number of pages7
JournalNeurocomputing
Volume129
DOIs
Publication statusPublished - 2014 Apr 10

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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