A hybrid MODWT-PSO-SVM model for time series forecasting
Jin Wang · 2024
Time series usually have certain trends and inherent patterns, so their future values can be predicted based on current information. The time series forecasting with high accuracy can provide decision basis for the relevant personnel, and then improve the efficiency of decision making. There are more and more researches on models for time series forecasting. In view of this, this paper proposes a hybrid MODWT-PSO-SVM model for time series forecasting. It is found that the constructed hybrid model can achieve better predictive performance. Furthermore, this paper investigates the impact of decomposition levels and the form of wavelet functions on the predictive performance of the constructed hybrid model. Research has shown that the predictive performance of the constructed hybrid model tends to decrease first and then increase with the increase of decomposition levels. This means that the predictive performance of the hybrid model varies with the number of decomposition layers, and an optimal number of decomposition layers can be found to achieve the best predictive performance. In addition, different forms of wavelet function also affect the prediction effect of the mixed model. When the Sym4 wavelet is used, the prediction effect of the hybrid model is the best. While when using Coif3 wavelets, the hybrid model has the worst prediction performance. Therefore, the predictive performance of the hybrid model can be improved by adjusting the number of decomposition layers and changing the form of wavelet functions. This research can provide a model reference for time series forecasting and supplement the existing research.