Hunt For Local Maxima, Minima, Plateau

Illustrates identification of local maxima, minima or plateau, for exit criteria for long processes.

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main executing reference usage: usage_proclivityStateTracking.m
* Each trend data point are to be of large time interval
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The objective is to illustrate identification of a local maxima, minima or plateau.
usage_proclivityStateTracking.m demonstrates a situation where the output of each point may have an interval of hours.

A braking condition may be posited to halt at certain conditions (braking or exit criteria), for example,
after a number of maxima, minima or plateau.

Given a program encapsulated by benchmarkSampleFunction.m (short naive process is used in place of long processes for illustrative purposes, you wish to replace with your own processes),
the demo illutrates the utility required for long processes where each round of output takes a long time.
User may wish to halt in condition of the detection of the next maxima. minima, plateau start or plateau end.
Further conditions may start N maxima, N minima, N plateau start/ end from now.

2nd demo (trivial): usageMain.m
* All trend data points are available
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Illustrates the mechanisms of the signal feature (maxima, minima or plateau) hunting.

* Caveat :
This build is not a perfect build, it serves as an illustration on which a framework may be built upon with the feature hunter functions, such as:
1. determineLocationsOfMaximaMinimaPlateauFromProclivityFeature.m : only when the a range of data points with it's signal trend/ profiles are available.
2. determineProclivity.m : given 2 data nodes or points, determine signal trend, ie. {'plateau', 'ascent', 'descent'}

Limitations:
The builds work for most fluctuating data signals. However, there are some signals which may cause some exceptions.
The author appreciates suggestions and errata. Please do not hesitate to send suggestions and send feedback for improvement for the framework construction to the email provided.

If the demo has more elegant presentation, please do not hesitate to suggest and send feedback to author.
Email: [e-mail].

Thank you.

Regards,
Michael Chan JT