Taiwanese export trade forecasting using firefly algorithm based K-means algorithm and SVR with wavelet transform

• 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.

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.