For my next step, I looked into different ways of predicting future temperatures using a method called Exponential Smoothing. This method is great for capturing patterns and trends in historical temperature data. I tested four specific techniques: Simple Exponential Method, Holt’s Method, and two variations of Holt-Winter’s method that consider different seasonal patterns. The goal was to see which one does the best job of predicting temperature changes over time.
Here’s a simple comparison of the results:
- Simple Exponential Method: This method, which looks at the historical average temperature without considering trends or seasons, had an error of around 24.38%.
- Holt’s Method: Adding trend information to the historical average temperature increased the error to about 37.79%.
- Holt-Winter’s with Additive Seasonality and Additive Trend: This model, considering both trend and seasonal variations, had a lower error of around 3.85%. It assumes that both the trend and seasons add up to influence temperature changes.
- Holt-Winter’s with Multiplicative Seasonality and Additive Trend: Similar to the previous one but assuming that seasonal variations have a multiplying effect, this method had an error of approximately 3.99%.
Conclusion: Among these methods, the one that considers both additive seasonality and additive trend performed the best, with the lowest error. This suggests that this model might be the most suitable for predicting temperatures based on the given historical data