Seasonal decomposition
Since the data has Seasonality, I performed a seasonal decomposition to see the extent of seasonality and what is the underlying trend in the data. To accomplish this, I used the statsmodels.tsa.seasonal model STL.
This process divided the data into a seasonal component, a trend component and a residual component. The complete dataset was a sum of these three components. The predictions from this model was better than that of ARIMA. The rmse score was 310.34. However, I believe the model was hindered by the presence of outliers in June 2015 and November 2023. Basically, the number of crimes accounted for in these two months was less compared to the usual monthly average as the entire month was not included In the dataset. To deal with this, in my further analysis, I will cut off the data from July 2015 to October 2023 to include entire months.
