Dataset split in time series forecasting 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:

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*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:

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*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?

Any time you use a dataset to fit some parameters, you are by definition fitting to that data.

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*Your training set fits each models parameters.

*Your validation set fits which model has the best hyperparameters.

So, a test set will be necessary here as the model you choose will have been fit to the validation data. This will ensure that your model is performing well out-of-sample.
I would also second Stephan's answer that 8 years of monthly data is not too small so the gain from ensuring generalization will out-weigh the loss of validation data.
