  This article studies the financial time series data processing for machine
learning. It introduces the most frequent scaling methods, then compares the
resulting stationarity and preservation of useful information for trend
forecasting. It proposes an empirical test based on the capability to learn
simple data relationship with simple models. It also speaks about the data
split method specific to time series, avoiding unwanted overfitting and
proposes various labelling for classification and regression.
