I am using exponential smoothing and using tableau for forecasting. The first model I included trend and removed seasonality and it predicted the number of cases going up but the quality I got according to tables was "Ok" (Tableau has 3 ranges to describe the quality of a model -Poor, OK and Good). The second model I removed trend and added seasonality to it, This showed the number of cases going down and the quality of the model was good according to tableau. Which model should I be using here, My professor says it's flattening the curve that's why it's going down in the second model while my teammates argue the first model should be the correct one.
I will suggest, first take some training data and make both models on that and compare the result on test data e.g
If you have data from 1st Jan 2020 to 28 Apr 2020.
Give the model, data from 1st Jan 2020 to 31st March(Or you can decide another range), make the model and forecast for April month.
And compare the results for both the models on April month(1st April to 28th April).
In this way, you will be able to argue better with results.