<ul data-eligibleForWebStory="true"><li>The fusion model of Random Forest and LSTM solves the problem of time series prediction, forecasting future trends based on historical data like temperature or stock prices.</li><li>The fusion model integrates features from lagged time series data to capture historical effects and uses LSTM to learn long-term dependencies in data sequences.</li><li>After predicting with Random Forest and LSTM, the fusion model averages the results to improve stability and accuracy of the predictions.</li><li>A visualization compares the true values with predictions from Random Forest, LSTM, and the fusion model, showing the fusion model's smoother trend.</li><li>A Mean Squared Error (MSE) Bar Chart illustrates that the fusion model has the smallest prediction errors, indicating its superiority over the other models.</li><li>An Error Distribution Diagram helps compare the error concentration and skewness of prediction errors between the models, with a closer-to-zero distribution indicating more stability.</li></ul>

Mastering Univariate Time Series Forecasting: Build a RF-LSTM Fusion Model with Real-World Cases &…

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