this is my first time working with timeseries, so I appreciate any comments and suggestions
In this notebook, I will walk through the following steps:- Data Exploration and Forecast Horizon: Initial examination of the dataset, determining the time period for predictions.
- Detrending: Removing long-term trends from the data.
- Deseasoning: Eliminating seasonal patterns to focus on the underlying data correlations.
- Post-Processing Analysis: Examining the data after detrending and deseasoning to ensure these processes were effective.
- Model Training and Evaluation: Training the LSTM model on the processed data and evaluating its performance.
- Predicting on Test Data: Making predictions on the test set to check the performance.
- Model Training on Full Data: Retraining the model on the entire dataset to improve its forecasting ability.
- Final Prediction: Generating final predictions for future data points using the trained model.
- Conclusion: Summarizing the findings, discussing the results, and suggesting potential improvements.