Hi everyone,
I’ve been following fast.ai for a while now and been trying to go through the courses, a bit on and off due to lack of time, but now I decided to start from scratch and do the Intro to ML course first.
Due to my work I’m also highly interested in time series (sensor data) and have been doing some work with Facebook’s Prophet with some ok results. Right now I’m interesting in trying to build a DL model, with the Rossmann approach and embeddings as a basis. So atm I’m trying to extract and engineer additional features (as my original data just includes time stamps and a measurement).
However, my biggest concern is how I would generate a forecast with predictions for future dates, say the next 30 days, after the model has been trained and evaluated on a test set? I.e how would I use it in production? Perhaps this has already been addressed in some specific lesson? If so I would be super grateful if someone could point me in the right direction.
I have found a pretty relevant thread on this: Predicting on a single row with Rossmann Data, but it doesn’t seem like they’ve found a solution yet. I also looked into some tutorials on machinelearningmastery.com and the approach he uses is to preprocess the time series, splitting the data into input and output vectors. So the length of the output vector defines the number of time steps to be predicted by the model. However, not sure if this is the right approach with the fast.ai library.
I hope my thoughts make sense:)