Hybrid model workflow

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Workflow for building a hybrid ARIMA-Adaptive LSTM model:

• Pre-process your data: Clean and preprocess your time series data, including handling missing values, transforming variables if necessary, and splitting the data into training and testing sets.

• Train ARIMA model: Fit an ARIMA model to the training data to capture the seasonal and trend components of the time series. Tune the ARIMA hyperparameters (e.g., order of differencing, autoregressive and moving average orders) using techniques like grid search or time series cross-validation.

• Generate ARIMA forecasts: Use the trained ARIMA model to generate short-term forecasts on the testing data.

• Compute ARIMA residuals: Calculate the residuals, which are the differences between the actual values and the ARIMA forecasts, for the training and testing data.

• Train Adaptive LSTM model: Use the ARIMA residuals as input to train an Adaptive LSTM model on the training data, capturing the residual patterns that ARIMA may have missed.

• Combine forecasts: Generate forecasts using the trained Adaptive LSTM model on the testing data and combine them with the ARIMA forecasts to obtain the final hybrid forecast.

• Evaluate and optimize: Evaluate the performance of the hybrid ARIMA-Adaptive LSTM model on the testing data using appropriate evaluation metrics, and fine-tune the model as needed by adjusting hyperparameters or model architecture.