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Prediction – the other dismal science?

An insightful person once said, “Prediction is like driving your car forward by looking only at the rearview mirror!”. If the road is dead-straight, you are good . . . UNLESS there is a stalled vehicle ahead in the middle of the road.

We should consider short-term and long-term prediction separately. Long-term prediction is nearly a lost cause. In the 80’s and 90’s, chaos and complexity theorists showed us that things can spin out of control even when we have perfect past and present information (predicting weather beyond 3 weeks is a major challenge, if not impossible). Even earlier, stochastic process theory told us that “non-stationarity” where statistics evolve (slowly or fast) can render longer term predictions unreliable.

If the underlying systems do not evolve quickly or suddenly, there is some hope. Causal systems (in Systems Theory, it means that no future information of any kind is available in the current state of the system), where “the car is driven forward strictly by using the rearview mirror”, outcomes are predictable in the sense that, as long as the “road is straight” or “curves only gently”, we can be somewhat confident in predicting a few steps ahead. This may be quite useful in some Data Science applications (such as in Fintech).

Another type of prediction involves not the actual path of future events (or the “state space trajectories” in the parlance) but the occurrence of a “black swan” or an “X-event” (for an elegant in-depth discussion, see John Casti, “X-Events: Complexity Overload and the Collapse of Everything’, 2013). For that matter, ANY unwanted event can be good to know about in advance – consider unwanted destructive vibrations (called “chatter”) in machine tools, as an example; early warning may be possible and very useful in saving expensive work pieces (“Instantaneous Scale of Fluctuation Using Kalman-TFD and Application…”). We find that sometimes the underlying system does undergo some pre-event changes (such as approach “complexity overload”, “state-space volume inflation”, “increase in degrees of freedom”, etc.) which may be detectable and trackable. However, there is NO escaping False Positives (and associated wastage of resources preparing for the event that does not come) or False Negatives (and be blind-sided when we are told it is not going to happen).

At Syzen Analytics, Inc., we use an explicit systems theory approach to Analytics. In our SYSTEMS Analytics formulation (“Future of Analytics – a definitive Roadmap”), the parameters of the system and its variation over time are tracked adaptively in real-time which tells us how long into the future we can predict safely – if the parameters evolve slowly or cyclically, we have higher confidence in our predictive analytics solutions.

Wanting to know the future has always been a human preoccupation – we see that you cannot truly know the future but in some cases, predictions to some extent are possible . . . surrounded by many caveats; more of “excuses” than definitive answers. Sounds a lot like a dismal science!

PG Madhavan, Ph.D.

Chief Algorist

Syzen Analytics, Inc.