README.md

AutoGP.jl

This package contains the Julia reference implementation of AutoGP, a method for automatically discovering Gaussian process models of univariate time series data, as described in

Sequential Monte Carlo Learning for Time Series Structure Discovery.
Saad, F A; Patton, B J; Hoffmann, M D.; Saurous, R A; Mansinghka, V K.
ICML 2023: Proc. The 40th International Conference on Machine Learning.
Proceedings of Machine Learning Research vol. 202, pages 29473-29489, 2023.

Whereas traditional Gaussian process software packages focus on inferring the numeric parameters for a fixed (user-specified) covariance kernel function, AutoGP learns both covariance kernel functions and numeric parameters for a given dataset. The plots below show two examples of online time series structure discovery using AutoGP, which discovers periodic components, trends, and smoothly-varying temporal components.

Installing

AutoGP can be installed using the Julia package manager. From the Julia REPL (version 1.8+), type ] to enter the Pkg REPL mode and run

pkg> add AutoGP

Alternatively, use the terminal command julia -e 'import Pkg; Pkg.add("AutoGP")'.

Tutorials

Please see https://probsys.github.io/AutoGP.jl

Developer Notes

Building Documentation

$ julia --project=. docs/make.jl
$ python3 -m http.server --directory docs/build/ --bind localhost 9090

Building From Clone

1. Obtain Julia 1.8 or later.
2. Clone this repository.
3. Set environment variable: export JULIA_PROJECT=/path/to/AutoGP.jl
4. Instantiate dependencies: julia -e 'using Pkg; Pkg.instantiate()'
5. Build PyCall: PYTHON= julia -e 'using Pkg; Pkg.build("PyCall")'
6. Verify import works: julia -e 'import AutoGP; import PyPlot; println("success!")'

Citation

@inproceedings{saad2023icml,
title        = {Sequential {Monte} {Carlo} Learning for Time Series Structure Discovery},
author       = {Saad, Feras A. and Patton, Brian J. and Hoffmann, Matthew D. and Saurous, Rif A. and Mansinghka, V. K.},
booktitle    = {Proceedings of the 40th International Conference on Machine Learning},
series       = {Proceedings of Machine Learning Research},
volume       = {202},
pages        = {29473--29489},
year         = {2023},
publisher    = {PMLR},
}