284 citations · 335 across the 7 of their papers we have counts for
8 papers
Neural Granger Causality
Alex Tank, Ian Covert, Nicholas Foti +2
While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently…
An Efficient ADMM Algorithm for Structural Break Detection in Multivariate Time Series
Alex Tank, Emily B. Fox, Ali Shojaie
We present an efficient alternating direction method of multipliers (ADMM) algorithm for segmenting a multivariate non-stationary time series with structural breaks into stationary…
An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality Discovery
Alex Tank, Ian Cover, Nicholas J. Foti +2
While most classical approaches to Granger causality detection repose upon linear time series assumptions, many interactions in neuroscience and economics applications are nonlinea…
A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Series
Christopher Xie, Alex Tank, Alec Greaves-Tunnell +1
Providing long-range forecasts is a fundamental challenge in time series modeling, which is only compounded by the challenge of having to form such forecasts when a time series has…
Granger Causality Networks for Categorical Time Series
Alex Tank, Emily B. Fox, Ali Shojaie
We present a new framework for learning Granger causality networks for multivariate categorical time series, based on the mixture transition distribution (MTD) model. Traditionally…
Identifiability and Estimation of Structural Vector Autoregressive Models for Subsampled and Mixed Frequency Time Series
Alex Tank, Emily B. Fox, Ali Shojaie
Causal inference in multivariate time series is challenging due to the fact that the sampling rate may not be as fast as the timescale of the causal interactions. In this context,…
Bayesian Structure Learning for Stationary Time Series
Alex Tank, Nicholas Foti, Emily Fox
While much work has explored probabilistic graphical models for independent data, less attention has been paid to time series. The goal in this setting is to determine conditional…
Streaming Variational Inference for Bayesian Nonparametric Mixture Models
Alex Tank, Nicholas J. Foti, Emily B. Fox
In theory, Bayesian nonparametric (BNP) models are well suited to streaming data scenarios due to their ability to adapt model complexity with the observed data. Unfortunately, suc…