A stock price prediction method based on meta-learning and variational mode decomposition

Stock price prediction is an important and challenging research topic, which has wide application prospects. Correct forecasting results can provide valuable guidance to investors and thus reduce the investment risk. To improve the prediction accuracy and obtain better prediction results, a new stock price prediction model called VML is proposed in this paper. First, the VML model slices the stock price series to obtain multiple window series, then uses variational mode decomposition (VMD) to decompose the window series to obtain multiple subseries. Unlike existing decomposition-based methods, VML decomposes the window series to solve the data leakage problem. Next, model-agnostic meta-learning (MAML) algorithm and long short-term memory (LSTM) network are applied to predict the subseries. A method of dividing the decomposed subseries into multiple tasks is proposed for the purpose of utilizing the MAML algorithm to train the initial parameters of the LSTM with good generalization ability. The initial parameters enable LSTM to fine tune dynamically to fit the latest data distribution of stock price data, which mitigates the impact of concept drift on prediction accuracy. Finally, the VML model merges the prediction results of the subseries to obtain the final predicted stock price. Experimental results on stock datasets of the Chinese Stock Market and the American Stock Market demonstrate that the proposed method improves the accuracy of prediction.