Don't miss tuned ARIMA, ETS (e.g. statsmodel). Include a library which has NBEATS, N-Hits (darts, gluonts). Tbh, Darts seems to cover all of them. Maybe DeepAR (gluonts). Most models don't do real multivariate forecasts, though.

Set up an honest evaluation and test all models. You can do some light pre-processing of the data, but don't spend too much time on it.

There aren't any magic tricks. Most methods won't beat a trivial baseline. Predicting the future usually does not work due to a missing predictive signal in the data. How is the model going to know what Musk will twitter tomorrow? The only thing that works is fitting boring seasonality and fitting the effect of holidays etc. You see that neither of these is actually about the future.

Let us know what worked in a critical evaluation in the end!

Here is a write-up https://www.sciencedirect.com/journal/international-journal-of-forecasting/vol/38/issue/4 of https://www.kaggle.com/competitions/m5-forecasting-accuracy . But note that in that competition it was more about fitting holidays and other tricks. There were a lot of zeros in the target. To predict the trend many people used a guessed fudge factor. Also look at the difference between public and private leaderboard to convince yourself that prediction of the top Kagglers in the world seemed to be a noisy mess for that data set. I'm afraid predicting the future isn't solved yet.