TY - JOUR
T1 - A latent information function to extend domain attributes to improve the accuracy of small-data-set forecasting
AU - Chang, Che Jung
AU - Li, Der Chiang
AU - Dai, Wen Li
AU - Chen, Chien Chih
N1 - Funding Information:
This research is partially supported by the National Science Council of Taiwan under grant NSC 101-22188-E-033-004- .
PY - 2014/4/10
Y1 - 2014/4/10
N2 - 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.
AB - 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.
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U2 - 10.1016/j.neucom.2013.09.024
DO - 10.1016/j.neucom.2013.09.024
M3 - Article
AN - SCOPUS:84893763013
VL - 129
SP - 343
EP - 349
JO - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
ER -