data prediction/forecast

It would be a big advantage to know what kind of the dependence is expected. If you have a mathematic model of the sensor then you can try to estimate the parameters of the model which make the best fit with your data. This is what is called Proni method.

If the dependence is supposed to be linear, then you can apply Kalman or Viener filtering or autoregression.

If nothing is known, then you can try to build a polynomial approximation.

Vladimir Vassilevsky

DSP and Mixed Signal Design Consultant

There is no such thing as a 'generic prediction algorithm'.

As a basis, you need to know the characteristics of the typical response curve over time. This would be in the form of some sort of expression whose actual values are dependant on some initial values/samples.

Exactly how you derive this typical response depends on what you're doing. Are you able to use previous history (ie. many many hours) in some sort of long-term adaptive algorithm in the device itself? Or can you derive a theoretical response (from physics and basic principles) that is valid for *all* sensors and just dependant on samples for the

I'm guessing you need to record a large number of observations (over many hours) and fit a response curve to the sample data and derive the parameters for an algorithm from that, which you can subsequently program into your device?!?

Regards, Mark

Do you mean it reaches (approaches) a steady state? If this is like heating a mass or storing a charge, the form is:

f(t) = A + B( 1 + exp(-kt) )

You can find better and better approximations for the factors as time goes by, if this is a one time event. Otherwise, calibrate.

f(t) = A + B( 1 - exp(-kt) )