Hello @zetyquickly, thank you so much for replying. My dependent variable is not temperature. It can be (for example) hourly electricity consumption. Now, the reason I encode the date-time variables such as hour of the day, day of the week, month, and year as categorical variables is because these are ‘fixed effects’. The hour of the day basically accounts for the fact that the human electricity consumption on any specific hour remains constant over a period of time because of their behavior. Like at 8 am in the morning, we can expect someone to use more electricity every day as they wake up and prep to go to work compared to electricity consumption at 1 am in the night.
Yes, this is a multivariate regression problem. Currently, my hyperparameter tuning (just training the model with electricity consumption as the dependent variable and temperature as the sole regressor., minus any categorical variables) indicates that I should be using a fixed length of 24, that is, the nth hourly electricity demand is dependent on the n-24 values of hourly temperature gives the best accuracy (measured by RMSE and MAPE).
I have already tested my model with categorical regressors using OLS and Categorical Boosting, but the LSTM model performs much better. I just want to confirm if I am thinking in the right direction or not. Very much appreciate any guidance you can provide