Temporally-Reweighted Chinese Restaurant Process Mixture Models

A nonparametric Bayesian method for clustering, imputation, and forecasting in multivariate time series data.

Installing

There are various ways to install this package. The easiest way is to pull the package from conda,

$ conda install -c probcomp trcrpm

For more information, see INSTALLING.md

Getting started

For tutorials showing how to use the method, refer to the tutorials directory.

Documentation

The API reference is available online. Use make doc to build the documentation locally (needs sphinx and napoleon).

References

Feras A. Saad and Vikash K. Mansinghka, Temporally-Reweighted Chinese Restaurant Process Mixtures For Clustering, Imputing, and Forecasting Multivariate Time Series. In AISTATS 2018: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research 84, Playa Blanca, Lanzarote, Canary Islands, 2018.

To cite this work, please use the following BibTeX reference.

@inproceedings{saad2018trcrpm,
author          = {Saad, Feras A. and Mansinghka, Vikash K.},
title           = {Temporally-reweighted {C}hinese restaurant process mixtures for clustering, imputing, and forecasting multivariate time series},
booktitle       = {AISTATS 2018: Proceedings of the 21st International Conference on Artificial Intelligence and Statistics},
series          = {Proceedings of Machine Learning Research},
volume          = 84,
pages           = {755--764},
publisher       = {PMLR},
address         = {Playa Blanca, Lanzarote, Canary Islands},
year            = {2018},
keywords        = {probabilistic inference, multivariate time series, nonparametric Bayes, structure learning},
}

License

Copyright (c) 2015-2018 MIT Probabilistic Computing Project

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.