I have a time-series (sensor data) with 2 variables
x y 2020-05-20 05:15:00 1192 355675 2020-05-20 05:30:00 1171 357600 2020-05-20 05:45:00 1260 392680
Rather than traditional time-series prediction with autoregression, my goal is to estimate
x and the timestamp.
I've gotten acceptable results using non-temporal models (regression, ensembles, etc), but this way I'm throwing away the time information. The other model I've considered using is Vector Autoregression - given that Granger Causality exists between the two variables - but my understanding is that this requires autoregression input from both variables.
What would be the best choice of model for the relationship between these variables over time? My goal is to be able to use only sensor
x, and estimate probably values of