Take a look at this book. It's available in the z-library Best wishes David Booth

Hello Vikneswaran,

Without more information as to the specific data set and research aims, there's no way to offer a focused recommendation beyond the type suggested by Professor Booth. Here's a partial listing of some possible methods, with brief explanations of each, which you may find helpful:

https://scikit-learn.org/stable/modules/linear_model.html

Good luck with your work.

Dear David Eugene Booth ,

Appreciate it! I will go through the book you recommended.

Thank you

Hello David Morse ,

Thank you very much for the link. I will look into it.

You will find many different examples here:

www.lerenisplezant.be/fitting.htm

Whether you can predict with accuracy or not, depends on the knowledge of the underlying mechanism!

Maybe you can consider the recursive least squares algorithm (RLS). RLS is the recursive application of the well-known least squares (LS) regression algorithm, so that each new data point is taken in account to modify (correct) a previous estimate of the parameters from some linear (or linearized) correlation thought to model the observed system. The method allows for the dynamical application of LS to time series acquired in real-time. As with LS, there may be several correlation equations and a set of dependent (observed) variables. For the recursive least squares algorithm with forgetting factor (RLS-FF), acquired data is weighted according to its age, with increased weight given to the most recent data.

A particularly clear introduction to RLS is found at: Karl J. Åström, Björn Wittenmark, "Computer-Controlled Systems: Theory and Design", Prentice-Hall, 3rd ed., 1997.

Years ago, while investigating adaptive control and energetic optimization of aerobic fermenters, I have applied the RLS-FF algorithm to estimate the parameters from the KLa correlation, used to predict the O2 gas-liquid mass-transfer, hence giving increased weight to most recent data. Estimates were improved by imposing sinusoidal disturbance to air flow and agitation speed (manipulated variables). The power dissipated by agitation was accessed by a torque meter (pilot plant). The proposed (adaptive) control algorithm compared favourably with PID. Simulations assessed the effect of numerically generated white Gaussian noise (2-sigma truncated) and of first order delay. This investigation was reported at (MSc Thesis):

Thesis Controlo do Oxigénio Dissolvido em Fermentadores para Minimi...