Forecasting the Amount of Book Usage by Time Series Techniques

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ฉริยะ อัครวรรณ จารี ทองคำ

Abstract

This research aims to compare an efficiency of time series techniques in predicting usage of library members' books. Data of book usage were collected from 2014 to 2017. Dewey Decimal of Classification was used to construct effective models consist of : (1) Artificial Neural Network (ANN) (2) Multi-Layer Perceptron Regression (MLPR) (3) Artificial Neural Network Regression (ANNR) (4) Support Vector Machine for Regression (SVMR) (5) Logistic Regression Analysis (LR) and (6) Reduced Error Pruning Tree (REPT). The models were compared with Sliding Windows method measured by values of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results showed that the prospective of library members book usage with the Support Vector Machine for Regression was the highest efficient. The MAE value was 9.42 and the RMSE was 11.46, respectively. 

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How to Cite
อัครวรรณฉ., & ทองคำจ. (2018). Forecasting the Amount of Book Usage by Time Series Techniques. Journal of Industrial Technology Ubon Ratchathani Rajabhat University, 8(2), 183-194. Retrieved from https://tci-thaijo.org/index.php/jitubru/article/view/163494
Section
Research Article

References

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