https://github.com/hendersontrent/tsgp

Simple Time Series Modelling Using Gaussian Processes
https://github.com/hendersontrent/tsgp

bayesian-inference bayesian-statistics gaussian-processes machine-learning statistics time-series

Last synced: 11 days ago
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Simple Time Series Modelling Using Gaussian Processes

• Host: GitHub
• URL: https://github.com/hendersontrent/tsgp
• Owner: hendersontrent
• License: other
• Created: 2024-05-04T10:57:36.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-05-05T23:21:53.000Z (6 months ago)
• Last Synced: 2024-05-06T11:48:27.455Z (6 months ago)
• Topics: bayesian-inference, bayesian-statistics, gaussian-processes, machine-learning, statistics, time-series
• Language: R
• Homepage: https://hendersontrent.github.io/tsgp/
• Size: 8.26 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

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README

        

# tsgp 

Simple, lightweight R package for fitting, visualising, and predicting

time-series data using Gaussian processes.

## Installation

You can install the development version of `tsgp` from GitHub using the

following:

``` r

devtools::install_github("hendersontrent/tsgp")

```

## Premise

`tsgp` implements the functionality presented in a [recent

tutorial](https://hendersontrent.github.io/posts/2024/05/gaussian-process-time-series/)

for modelling time-series data with Gaussian processes. Currently, only

the univariate setting is supported. `tsgp` works on a [structural time

series](https://www.sciencedirect.com/science/article/abs/pii/S0169716105800458)

perspective. That is, by decomposing a time series into its constituent

statistical parts (e.g., trend, seasonality, noise), one can model each

component independently before combining them to form the complete

picture of temporal dynamics. This is not only intuitive, but it is also

highly transparent—meaning that intelligent and justifiable modelling

decisions must be made in order to appropriately capture the data

generating process.

`tsgp` is extremely lightweight in both its dependencies and

computational approach. If you are seeking a more rigorous or flexible

approach to using GPs for time-series analysis, please look into

[`Stan`](https://mc-stan.org),

[`GPy`](https://gpy.readthedocs.io/en/deploy/),

[`GauPro`](https://github.com/CollinErickson/GauPro), [Tensorflow

Probability](https://www.tensorflow.org/probability), or

[`GaussianProcesses.jl`](https://github.com/STOR-i/GaussianProcesses.jl).

## Functionality

Currently, `tsgp` supports the following covariance functions (kernels):

- Exponentiated quadratic (squared exponential)

- Rational quadratic

- Periodic

- Linear

`tsgp` flexibly enables composite kernels (either through addition or

multiplication) to be constructed and is actively encouraged to

appropriately model complex temporal dynamics.

`tsgp` also includes functions for computing and visualising draws from

Gaussian process priors and posteriors, visualising covariance matrices,

and plotting predictions.

## Performance

`tsgp` is *extremely* fast at what it does. Well, as fast as it can be

given the computation time involved in computing a GP posterior. `tsgp`

implements well-known methods for efficiency and stability, such as

using the Cholesky factorisation instead of computing a matrix inverse

directly. A full model with trend, seasonality, and noise can be

calculated on a time series of $T = 1000$ time points in a few seconds.

This makes it an ideal tool for iterative model building and principled

time-series exploration.