284 citations · 587 across the 49 of their papers we have counts for
8 papers · 1 filter
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…
Joint Structural Break Detection and Parameter Estimation in High-Dimensional Non-Stationary VAR Models
Abolfazl Safikhani, Ali Shojaie
Assuming stationarity is unrealistic in many time series applications. A more realistic alternative is to allow for piecewise stationarity, where the model is allowed to change at…
Structural Break Detection in High-Dimensional Non-Stationary VAR models
Abolfazl Safikhani, Ali Shojaie
Assuming stationarity is unrealistic in many time series applications. A more realistic alternative is to allow for piecewise stationarity, where the model is allowed to change at…
The Multivariate Hawkes Process in High Dimensions: Beyond Mutual Excitation
Shizhe Chen, Ali Shojaie, Eric Shea-Brown +1
The Hawkes process is a class of point processes whose future depends on their own history. Previous theoretical work on the Hawkes process is limited to a special case in which a…
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…
In Defense of the Indefensible: A Very Naive Approach to High-Dimensional Inference
Sen Zhao, Daniela Witten, Ali Shojaie
A great deal of interest has recently focused on conducting inference on the parameters in a high-dimensional linear model. In this paper, we consider a simple and very naïve two-s…
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,…