Logistics Demand Forecasting with 20k Different Time Series
First of all, there is no guarantee that you can beat baseline with ML, this is something you should take into account.
You also might find a solution for some particular Type Of Demand due to some inner behaviour, but this might not generalize well for the rest of the cases.
I would also try to use Facebook`s Prophet
From the authors:
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
It might work well out of the box, without any major tweaks. You can also run in in parallel (because the process of fitting of model is kinda automatic), which is helpful when you have this much time series.
Here is a link on how to implement it efficiently (but note that original docs are quite good as-well)
https://towardsdatascience.com/implementing-facebook-prophet-efficiently-c241305405a3
And a post regarding parallel execution
https://medium.com/spikelab/forecasting-multiples-time-series-using-prophet-in-parallel-2515abd1a245
I would also spend more time analyzing results: can you check the error between different types of demand, search for the particular cases when prophet beats baseline and try to understand why its so and so on.