Forecast-Models-Showdown

Different forecast approaches side by side comparison on a dataset.

The purpose of this notebook is to find the optimal approach for forecasting a timeseries.

Model Building

First, I transformed the column variable into variables. I also split the data into train and tests sets with a test size of 20%.

I tried three different models and evaluated them using RMSE and MSE. I chose them because it is relatively easy to interpret and outliers aren’t particularly bad in for this type of model.

Model Performance

I tried three different models:
ARIMA
Long Short Term Memory
Prophet

ARIMA model far outperformed the other approaches on the test sets, but the models weren't tuned at their maximum performance.

Model	RMSE	MSE
ARIMA	0.765449	0.585912
LSTM	1.328145	1.763970
Prophet	1.591280	2.532170