This is a bit DSP-related: so if you turn your non-stationary time series into a stationary process, you'll probably see that it is not periodic.. This is an issue for Fourier-based techniques because they are not local in frequency. Now, besides wavelets (some types are causal btw), which other causal techniques can you use? (and ARMA is not it). I tried Empirical Mode Decomposition (HHT), but that's not causal; I tried Intrinsic Time-scale Decomposition: not causal either. Wavelets are pretty old and I would think something better would have been "discovered" by now? Does anyone know of a good causal signal processing technique that deals well with non-periodicity? Thanks!!
I know only that Jurik's JMA is good causal filter, better than Kalman and Volterra filters, but I don't know for sure what algorithm inside - it's black box. Does anybody know better causal filter?
Wavelets and Kalman filtering.
The issue with wavelets is that you'll have some boundary distortions so be careful when exploiting the results.