Fit data as you like

• paramonte

  4 241 9.2 Fortran

  ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C.

  

Can you provide more information about your data? How many dimensions? 1D? Also, could you elaborate on what you mean by fitting a Gaussian to the time series data? Do you mean a Gaussian process? My lab has written a fast generic Bayesian optimizer and sampler library, in pure modern Fortran, that can not only find the best-fit parameters of your time-series model (whether polynomial, sin, ...), but can also put constraints on the uncertainties associated with the parameters. Writing a generic likelihood function for polynomial or other types of fits is quite easy. Once you write it, you simply compile and link it with this library to find the best-fit parameters of each model. The prebuilt ready-to-use versions of the library are also available on the GitHub release page.I would be happy to help you further with writing the polynomial/sin models and fitting them to your data with this library. But some further information is needed from your side to write the objective functions for different models (poly, sin, ...).

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

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