Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data

1 code implementation • 16 Apr 2021 • Andrew R. Lawrence, Marcus Kaiser, Rui Sampaio, Maksim Sipos

We propose a flexible and simple to use framework for generating time series data, which is aimed at developing, evaluating, and benchmarking time series causal discovery methods.

Causal Discovery Time Series

DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures

no code implementations • 12 Jul 2018 • Andrew R. Lawrence, Carl Henrik Ek, Neill D. F. Campbell

We present a non-parametric Bayesian latent variable model capable of learning dependency structures across dimensions in a multivariate setting.