[D] Pipeline for data processing in time series forecasting?

What is the correct pipeline for data processing when conducting time series forecasting? Should we begin with data normalization/standardization, followed by feature selection, and then split the data into training, validation, and test sets? Or is it advisable to initially split the data to prevent spill-over effects?

I'm concerned about the possibility of training my model on (part of) the test data, which could result in spill-over effects. However, if the recommended approach is to split the data first and …

data data normalization data processing effects feature feature selection forecasting machinelearning normalization pipeline processing series standardization test time series time series forecasting training validation