I am trying to make a sales forecasting model to predict into the near future.
My data is monthly and of time series type as seen below:
2013-1-1 : 2000$
2013-2-1 : 6000$
. . .
2021-1-1 : 3000$
What I am thinking of trying first is a SARIMA model because my data have strong seasonality.
My concern is:
- How should I split the dataset for training purposes?
The split should be chronological and I am thinking of doing a train/validation split and choosing the best model hyperparameters based on the validation RMSE. However, I have been advised to use a test set too for my model evaluation which is something I am cautious of.
Since I am purely choosing a model based on my validation data is a test set here really necessary? By doing this split I am using less data for training which I believe will result in a worse model.
What are your thoughts on this?