Forecasting Non-Stationary Processes

22 Aug 2015 14:14

Some non-stationary processes are in fact easy to forecast: periodic ones, for example, are strictly speaking not stationary. An ergodic Markov chain started far from its invariant distribution is also non-stationary, but easy to predict (it will approach the stationary distribution). Both of these cases are conditionally stationary, which I think is all that's really needed.

What's more interesting is the problem of so to speak really non-stationary processes. It's hard to imagine that there is any way to truly predict an arbitrary non-stationary process. (Basically: as soon as you think you have established a trend-line, the Adversary can always reverse the trend, without creating any problems of consistency with earlier data.) If you can constrain the class of allowable non-stationary processes, however, then something might be possible. Alternately, one might lower expectations, not to actually predicting well, but to predicting with low regret.

I actually have an Idea about using model averaging here, but need to find the time to work on it.

See also: Ensemble Methods in Machine Learning; Low-Regret Learning; Time Series; Universal Prediction

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