In the chemical process industry a lot of sensor data is captured (flows, levels, pressures, temperatures etc). This data can be described as time series non-stationary. Looking at a typical pattern, one might find a trend pattern (without seasonality or little = summer/winter or day/night) particular when looking at monotonic evolutions (such as fouling). But often the candidate features don't exhibit a trend pattern (unless the feature is used by the operator to compensate). if you have a plant issue that can be described by a certain measurement, the question often arrises: which other (set of) variable(s) caused this? Most analysis tools, for example PCA/LDA/Random forest, rely on stationary data. I want to avoid trying to create a state space model to compensate for non-stationary situations. Alternatively I could perform a double exponential smoothing, but that is only usable if I have a clear trend.
My question is: how to transform this data in such a way that I can use it for analysis such as PCA for example (and not using wavelet PCA)?
Thanks!
