discontinuum is a middleware for developing Gaussian process (GP) timeseries models. Why might we want a middleware? GP's are an elegant way to model timeseries with uncertainty. In many cases, we can represent a complex timeseries as a GP with only a few lines of math. However, fitting GP's is numerically intense, $\mathcal{O}(n^3)$ complexity. There are several optimizations that take advantage of simplifying assumptions, different algorithms, or GPUs, but each has different tradeoffs. Ideally, we could write the mathematical model once, then run it on whichever "engine" is best suited for a particular problem. With every model comes a lot of standard utility functions, and the goal of discontinuum is to package these different model applications, engines, and utilities into a single ecosystem.

pip install discontinuum

Only one for now.

LOAD ESTimator (LOADEST) is a software program for estimating river constituent timeseries using surrogate variables (covariates). For example, estimating nitrate concentration based on date and streamflow. However, LOADEST has several serious limitations---it's essentially a linear regression--- and it has been all but replaced by the more flexible Weighted Regression on Time Discharge and Season (WRTDS), which allows the relation between target and covariate to vary through time. loadest-gp takes the WRTDS idea and reimplements it as a GP.

from loadest_gp import LoadestGP()

model = LoadestGP()
model.fit(target, covariates)
model.plot(covariates)

Currently, the only engine is pymc's marginal likelihood implementation.

mindmap
  root((discontinuum))
    data providers
      USGS
      EPA
      etc
    engines
      PyMC
      Tensorflow
      PyTorch
    utilities
      pre-processing
      post-processing
      plotting
    models
      loadest-gp
      your own