Moving beyond the training and evaluation phase, our focus now shifts to assessing the predictive accuracy of the LSTM model. We employed the Root Mean Squared Error (RMSE) metric to quantify the difference between predicted and actual values. This metric provides a measure of the model’s ability to capture the variability in ‘Temp_Avg,’ offering valuable insights into the overall accuracy of the predictions.
RMSE Results: The calculated RMSE for the training set stands at 246.34, while the testing set exhibits an RMSE of 278.79. These values represent the square root of the average squared differences between predicted and actual temperatures, indicating the model’s overall precision. A lower RMSE signifies better accuracy, and while these values provide a quantitative measure, they should be interpreted in the context of the dataset’s characteristics and the specific application.
we assessed the predictive accuracy of our LSTM model using the RMSE metric. The values obtained serve as a benchmark for the model’s performance, offering insights into its ability to make accurate predictions. While RMSE provides a quantitative measure, the true effectiveness of the model should be considered in conjunction with domain knowledge and the specific requirements of the application. In the subsequent posts, we’ll explore further refinements, potential enhancements, and practical applications of our LSTM time series model.