I have done feature engineering on a single variable time series data (spare parts usage), then I turn the time series data into supervised machine learning problem. I have trained and test on the transformed new dataset. My question is how to apply the model to predict one time step, multi time steps into the future? All features are calculated over prior data points, such as lag features, moving window statistics, such as max, min, mean, median over for example, last 5, 10, 15 days.
If you have working examples in Python or any white papers with example, that will be great. Look forward to your help Shaun
