In the final stage of my analysis, I developed an ARIMA model specifically designed for forecasting the “Temp_Avg” variable in time series data.
Stationarity Check:
In this step, I performed the Augmented Dickey-Fuller (ADF) to check if the temperature data behaves consistently over time. The p-value obtained from the Augmented Dickey-Fuller (ADF) test is used to determine the stationarity of a time series. p-value ≤ 0.05: If the p-value is less than or equal to 0.05, we reject the null hypothesis. This indicates that there is enough evidence to suggest that the time series is stationary. In other words, the time series has no unit root, and it is relatively stable over time.
In this case, with a p-value of 4.564129009307574e-14 (a very small value close to zero), we would reject the null hypothesis. This suggests that our temperature time series is likely stationary, which is a good prerequisite for applying models like ARIMA that assume stationarity.
Hyperparameters Best Params Selection:
The next step is to find the best parameters for our ARIMA model. This involved testing different combinations of parameters (p, d, q) to see which set provides the most accurate predictions for our dataset. The combination that resulted in the lowest Root Mean Square Error (RMSE) was chosen as the best parameters. In this case, the best parameters were (4, 1, 3).
Final Model on the Entire Dataset with Best Params:
With the best parameters identified, I built the final ARIMA model using the entire temperature dataset. The model was trained on the historical data, and then it is used to forecast future temperatures. The actual and predicted values were plotted, visually showing how well the model captures the patterns in the temperature data. The red line represents the forecasted temperatures, while the blue line represents the actual observed temperatures. This final model allows us to make predictions for the entire dataset and provides a useful tool for understanding future temperature trends based on historical patterns.
