Fractal memory and deep learning: A new paradigm for stock market prediction

The lack of a steady state, extreme nonlinear behavior, and disruption in capital time-based collection make stock market forecasting very difficult. Therefore, making informed decisions in the financial markets requires accuracy. price predicting forecast. Because they are generally unable to adequately represent such intricate dynamics, conventional models have increasingly given way to hybrid deep learning strategies. To obtain extended-period memory repercussions, this work incorporates fractal aspects of the monetary time series into deep learning templates using the sum of the being rolled Hurst exponent. A couple of crossed prediction frameworks—LSTM-with-Hurst, CNN-with-Hurst, and GRU-with-Hurst—are created. With the instruction , outcomes of 0.9813 and 0.9812, and examining  figures of 0.8088 and 0.8022, for instance, the LSTM-with-Hurst and GRU-with-Hurst avatars demonstrate good predictive accuracy, according to the observed data. The CNN-with-Hurst approach, on the other hand, has good training results ( = 0.9869), but poor test results ( = 0.9869), suggesting poor generalizability. The results show that LSTM-with-Hurst and GRU-with-Hurst are strong and reliable models for financial market prediction, and that combining fractal features with continuous deep learning patterns greatly improves the overall speed of stock price-based forecasts.