How to predict course of a graph

I've got measuring data from sensors and the belonging times in an excel table.

Now I want to make a statement when an assumed value could be fall below or exceeds an assumed value. (predictive maintenance)

I want to do this with machine learning methods.

Which toolbox should i use? And whrere can i specify the limits which matlab should use to give me a response of when the case of reaching a limit yould occur?

Are there any possibilities and how can I get there?

Thank you for answers!

[John D'Errico]

To quote one of my favorite authors...

“In the space of one hundred and seventy six years the Lower Mississippi has shortened itself two hundred and forty-two miles. That is an average of a trifle over a mile and a third per year. Therefore, any calm person, who is not blind or idiotic, can see that in the Old Oölitic Silurian Period, just a million years ago next November, the Lower Mississippi was upwards of one million three hundred thousand miles long, and stuck out over the Gulf of Mexico like a fishing-pole. And by the same token any person can see that seven hundred and forty-two years from now the Lower Mississippi will be only a mile and three-quarters long, and Cairo [Illinois] and New Orleans will have joined their streets together and be plodding comfortably along under a single mayor and a mutual board of aldermen. There is something fascinating about science. One gets such wholesale returns of conjecture out of such a trifling investment of fact.”

Mark Twain, from Life on the Mississippi

And you want a computer to automatically predict what will happen in the future, using a model based on only what it can see?

Extrapolation is best done when the user can provide an intelligent model of the system under study. Lacking any reasonable model of the process, and you should return and think about that quote above.

Answer 1

[Gaurav Garg]

Hi,

Statistics and Machine Learning Toolbox and Curve Fitting Toolbox provide apps and functions for fitting curves and surfaces to data, and to describe, analyse and model the data. You could apply linear regression/nonlinear fitting, or you could extract features from data using supervised/unsupervised learning techniques by identifying a subset of features (if your data exhibits some common pattern) and applying machine learning models.

Also, the above told toolboxes provide you with apps as well, information for which can be found here (link1and link2).