Taiwanese export trade forecasting using firefly algorithm based K-means algorithm and SVR with wavelet transform. (September 2016)

• Record Type: Journal Article
• Authors: Kuo, R.J.; Li, P.S.
• Is Part Of: Computers & industrial engineering. Volume 99(2016)
• Page Start: 153
• Page End: 161
• Publication Date: 2016-09
• Subjects: Cluster analysis -- Firefly algorithm -- Support vector regression -- Forecasting -- Wavelet transform; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2016.07.012
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Propose a model integrating wavelet transform, FA based K -means and FA based SVR. FA based K -means algorithm is employed for clustering data. FA based SVR is applied for forecasting. The results show that the proposed model outperforms other models. Abstract: In order to develop a prediction system for export trade value, this study proposes a three-stage forecasting model which integrates wavelet transform, firefly algorithm-based K -means algorithms and firefly algorithm-based support vector regression (SVR). First, wavelet transform is utilized to reduce the noise in data preprocessing. Then, the firefly algorithm-based K -means algorithm is employed for cluster analysis. Finally, a forecasting model is built for each cluster individually. For evaluation, this study compares methods with and without clustering. In addition, both non-wavelet transform and wavelet transform for data preprocessing are investigated. The experimental results indicate that the forecasting algorithm with both wavelet transform and clustering has better performance. Besides, firefly algorithm-based SVR outperforms the other algorithms.