Attention-LSTM Multi-Scale Stock Index Prediction Model

다국어 초록 (Multilingual Abstract)

Financial time series exhibit considerable noise, non-stationarity, and intricate structural variations, presenting formidable Financial time series have a lot of noise, non-stationarity, and complex structural changes, making it very challenging to predict short-term stock market movements. This study examines how explicitly delineating the multi-scale characteristics of market dynamics can increase the precision and stability of predictions. The temporal attention process can be used with wavelet-based decomposition to make a multi-scale Attention-LSTM model. This makes it possible to split long-term trend data from short-term change patterns. At the same time, it shows useful historical times. Tests show that the suggested model is always better than LSTM, Attention-LSTM, and transformer when it comes to RMSE, MAE, MAPE, and direction accuracy. These results show that combining multi-scale models with temporal attention is a viable way to make stock index predictions more reliable.