Exponential Smoothing
Given the failure of the ARIMA model to capture the seasonality and trends in the data, I studied different models that can be used to better model the data. One of the models I found was Exponential Smoothing State Space model. This model is best to use when the data has seasonality or when there is a general pervasive trend in the data.
Since the data at hand had both these features, I applied the ETS model and found better results. Initially, I split the data 80-20 into training and testing data. This meant the data from 2015 up to mid 2022 would be considered training data and the model will try to predict the trend from mid 2022 to mid 2023.
This allowed me to understand whether the model performs well or not and to decide how to change the model before trying to forecast into the future.
I used the evaluation metrics MSE and MAPE to compare the performance of each model.
The MSE for ARIMA predictions was 89911.182 which is a very large value. Comparatively, the MSE value for Exponential smoothing was 1148.91 for accidents data. This is a large improvement in prediction results.
