DWT-TimesNet: An Improved Model for Wheat Price Long-Time Series Forecasting
Mingwei Lei, Xingfen Wang · 2023
Long-term forecasting of wheat prices is important for agricultural production, market policy making and food security assurance. In this study, an improved long time series forecasting model for wheat price, DWT-TimesNet, is proposed, which is deeply improved on the basis of the original TimesNet model. The main improvement lies in the use of Discrete Wavelet Transformation (DWT) instead of the original Fast Fourier Transformation (FFT) for time series decomposition. Since the FFT has certain limitations in dealing with non-smooth signals, such as fixed frequency resolution and the inability to provide time and frequency information at the same time, the DWT overcomes these shortcomings through the characteristics of multi-resolution analysis, and is able to localize signals at different scales and provide a more refined time-frequency representation. In the improved DWT-TimesNet model, we use the mean plus standard deviation method to dynamically determine a threshold that is used to filter valid cycles, and cycles corresponding to amplitudes above this threshold are considered valid cycles. This improvement allows the model to be more flexible in adapting to signal variations, and improves the model’s ability to capture the periodic properties of the signal compared to the fixed cycle selection mechanism in the original method. Furthermore, the improved method returns an additional parameter k indicating the number of selected cycles, which provides more information for subsequent processing and increases the interpretability of the method. To validate the effectiveness of the DWT-TimesNet model, we compare it with the original TimesNet model and other mainstream time series forecasting models. The experimental results show that the DWT-TimesNet model achieves remarkable results in wheat price forecasting, with results far ahead of other models. This research is not only academically significant, but also has important implications and benefits for practical production and life. The accurate prediction of wheat prices can help farmers rationalize their planting plans and reduce production costs; the government can formulate reasonable market policies based on the prediction results and stabilize market prices; and it also helps to achieve food security guarantee.