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

研究成果: Article

16 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)343-349
頁數7
期刊Neurocomputing
129
DOIs
出版狀態Published - 2014 四月 10

    指紋

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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