Optimizing AdaBoost for Bitcoin Price Prediction Based on Long and Short Term Memory Network Models

In this paper, a hybrid time series analysis model for bitcoin price forecasting is constructed by introducing a long short-term memory network (LSTM) to deeply optimize the traditional AdaBoost integrated learning model. Experimental results show that the fusion model exhibits excellent dual adaptability in the field of financial time series forecasting: in the training phase, the model achieves a high degree of fit to historical data with an excellent performance of MAE 1.3574, MSE 3.4279, and MAPE 0.412%, and its coefficient of determination, R, breaks through 0.97105, which intuitively verifies the ability of the LSTM module in capturing the non-linear time-series characteristics of the The synergy between the LSTM module and the AdaBoost framework in strengthening the generalization ability of the model; in the testing phase, although facing the challenge of increased market volatility, the model still maintains a stable performance of MAE 1.914, RMSE 2.7893 and MAPE 0.586%, especially the prediction error rate is continuously controlled within 1% of the industrial-grade accuracy threshold, and its test set R value of 0.88675 significantly exceeds the benchmark interval of 0.6-0.8 for conventional prediction systems, confirming the strong explanatory power and robustness of the model in unknown data environments.