An improved deep temporal convolutional network for new energy stock index prediction.

Accurate prediction of the stock indexes in the new energy market is of significant importance to both investors and policymakers. However, in response to the volatility and uncertainty characteristic of the new energy market, most scholars currently focus on training prediction methods using features from a single time scale, which cannot capture the fluctuations of new energy stock indexes under different time scales. Therefore, in this paper, a hybrid deep learning model Multi-kernel Parallel TemporalNet (MKP-TemporalNet) is proposed for predicting new energy stock indexes. This model initially incorporates an attention mechanism to calibrate the feature importance of multivariate time series dynamically, and then combines the characteristics of improved Temporal Convolutional Networks (iTCN) and Bidirectional Gated Recurrent Units (BiGRU) to enhance prediction accuracy effectively. In particular, the novelty of the iTCN lies in the development of a multi-kernel parallel convolution structure within a residual layout at the core of the temporal convolution module, to address the low efficiency of traditional TCN's single kernel convolution in extracting temporal features from input sequences at different time scales. Results from evaluating MKP-TemporalNet against several popular machine learning models on six new energy stock indexes confirm its predictive effectiveness in the new energy sector. [ABSTRACT FROM AUTHOR]