Interpretable LIM-GRLST Hybrid Deep Learning Approach for Financial Cryptocurrency Trends Prediction

Bitcoin and other cryptocurrency markets are volatile by nature, which provides an opportunity as well as a threat to an investor and researcher. The correct forecasting of prices is an important factor in making informed decisions when in such dynamic conditions. The paper presents a explainable LIM-GRLST framework of Bitcoin price forecasting on a one-step-ahead basis with deep learning. The baseline LSTM and GRU models were trained initially and then hybrid models that combine the complementary advantages of the two models were trained. A weighted hybrid also exhibited relatively small improvements, whereas the neural hybrid produced the highest results (MAE = 0.0209, MSE = 0.0008, RMSE = 0.0279, R 2 = 0.9897), which were tested on an unknown test set. Standard post-hoc methods, permutation importance and LIME, were used to provide the feature-level explainability, indicating closing, high, and low prices as the most important predictors. The article focuses on enhancing engineering within frameworks of hybrid and hyperparameter optimization over a novel methodology, and is shown to be applicable in practice to cryptocurrency forecasting.