Optimizing Stock Market Predictions: A Comprehensive 3-Stage Model with Residual Modeling

We present a novel three-stage model that leverages residual modeling techniques to forecast time series in financial markets. To evaluate the model's performance, we use a comprehensive set of seven financial indexes covering approximately 2272 trading days for each index, including 453 days for testing. Our results show that our proposed model consistently outperforms the reservoir computing (RC) model, which has previously demonstrated superior performance to deep learning models. Our proposed model is more accurate making it an ideal solution for time-series forecasting.