I'm trying to solve a time series forecasting problem, specifically I want to use historical data with an aspect of COVID-19 impact on global and regional aviation, number of new COVID-19 cases per country of origin (country of registration for the aircraft), and monthly average temperature in said country.
I wanted to ask for input on a tool that blends support datasets with a historical time series well? Ideally, the average temperature and 2-4 week lag of COVID-19 cases in a country would be input to the model along with each aircraft 12-24 month history.
I'm specifically looking at Tensorflow Probability and their Structural Time Series: https://blog.tensorflow.org/2019/03/structural-time-series-modeling-in.html and https://www.tensorflow.org/probability/examples/Structural_Time_Series_Modeling_Case_Studies_Atmospheric_CO2_and_Electricity_Demand#model_and_fitting
SK Time could also be an option: https://www.sktime.org/en/latest/examples/01_forecasting.html#Detrending
Facebook's Prophet also seems to be able to model this: https://facebook.github.io/prophet/docs/seasonality,_holiday_effects,_and_regressors.html#seasonalities-that-depend-on-other-factors
It could probably also be seen as a regression problem, by engineering features of all the time series datasets and feeding it into a regression model to predict monthly flights?
Anyway, I'm asking this question looking for some inspiration and possibly a tool that I haven't seen before could provide other options.