Embarking on a journey into predictive modeling, we utilized Long Short-Term Memory (LSTM) neural networks to forecast ‘Temp_Avg’ in the ‘climate’ dataset. The data was preprocessed, normalized, and split into training and testing sets. The LSTM model, with a sequence length of 12 time points, was trained over 50 epochs, each revealing insights into the model’s learning process. The training and testing losses were monitored, providing a measure of the model’s performance on seen and unseen data, respectively.
Training and Evaluation Results: The model underwent 50 epochs, with training loss steadily decreasing over time, reaching 0.00876, and testing loss plateauing at 0.01122. This performance indicates that the model successfully learned patterns from the training data and generalized well to the testing data. The next step involved making predictions on both the training and testing sets, followed by inverse transforming the predictions to their original scale. Visualizing the results unveils the model’s ability to capture the underlying patterns in ‘Temp_Avg’ across the entire dataset.
we delved into the training and evaluation phase of our LSTM time series model. The decreasing training loss and comparable testing loss showcase the model’s capability to learn and generalize, laying the groundwork for accurate predictions. The visual representation of predicted and actual values sets the stage for a comprehensive analysis, and in the next post, we’ll delve deeper into the model’s predictive accuracy by calculating the Root Mean Squared Error (RMSE) for both the training and testing sets.