Software
All software for the publications listed below can be found here.
Modeling Large Collections of Sparsely Sampled Time Series
A plethora of data streams is being collected in a growing number of fields, ranging from social networking and e-commerce to high-resolution brain imaging, based on technologies and infrastructures previously not available. In this era of big data, these abundant time series present new challenges: the individual data streams are often sparsely sampled such that each alone does not provide sufficient data for accurate inferences. However, the structured relationships between them presents an opportunity to share information. We term this the “big p, infrequent n” challenge of big data time series.
Models of Sparsely Sampled Time Series
C. Glynn, and E.B. Fox, "Dynamics of Homelessness in Urban America," Annals of Applied Statistics, 2019.
C. Xie, A. Tank, and E.B. Fox, "A Unified Framework for Missing Data and Cold Start Prediction for Time Series Data," NIPS Time Series Workshop (awarded Best Oral Presentation), December 2016.
S. Aldor-Noiman, L.D. Brown, E.B. Fox, and R.A. Stine "Spatio-Temporal Low Count Processes with Application to Violent Crime Events," Statistica Sinica, October 2016.
Y. Ren, E.B. Fox, and A. Bruce "Achieving a Hyperlocal Housing Price Index: Overcoming Data Sparsity by Bayesian Dynamical Modeling of Multiple Data Streams" Annals of Applied Statistics, 2016.
Learning Directed and Undirected Graphs of Time Series
A. Tank, I. Covert, N. Foti, A. Shojaie, and E.B. Fox, "Neural Granger Causality," IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021.
A. Tank, X. Li, E.B. Fox, and A. Shojaie, "The Convex Mixture Distribution: Granger Causality for Categorical Time Series," SIAM Journal on Mathematics of Data Science, 2021.
A. Tank, E.B. Fox, and A. Shojaie, "Identifiability and estimation of structural vector autoregressive models for subsampled and mixed-frequency time series," Biometrika, 2019.
S. Ainsworth, N. Foti, A.K.C. Lee, and E.B. Fox, "oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis," International Conference on Machine Learning, July 2018.
A. Tank, I. Covert, N. Foti, A. Shojaie, and E.B. Fox, " An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality Discovery," NIPS Time Series Workshop, December 2017.
A. Tank, E.B. Fox, and A. Shojaie, "Granger Causality Networks for Categorical Time Series," KDD Workshop on Mining and Learning from Time Series, August 2016.
A. Tank, N. Foti, and E.B. Fox, "Bayesian Structure Learning of Stationary Time Series," Conference on Uncertainty in Artificial Intelligence (UAI), Amsterdam, Netherlands July 2015.
Scalable Bayesian Inference
C. Aicher, N. Foti, and E.B. Fox, "Adaptively Truncating Backpropagation Through Time to Control Gradient Bias," Conference on Uncertainty in Artificial Intelligence (UAI), July 2019.
C. Aicher, Y.-A. Ma, N. Foti, and E.B. Fox, "Stochastic Gradient MCMC for State Space Models," SIAM Journal on Mathematics of Data Science, 2019.
J. Baker, P. Fearnhead, E.B. Fox, and C. Nemeth, "sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo," Journal of Statistical Software, 2019.
J. Baker, P. Fearnhead, E.B. Fox, and C. Nemeth, "Control variates for stochastic gradient MCMC," Statistical and Computing, 2019.
Y.-A. Ma, E.B. Fox, T. Chen, and L. Wu, "Irreversible Samplers from Jump and Continuous Markov Processes," Statistics and Computing, 2019.
J. Baker, P. Fearnhead, E.B. Fox, and C. Nemeth, "Stochastic Sampling from the Probability Simplex," Neural Information Processing Systems (NeurIPS), 2018.
A. Tank, E.B. Fox, and A. Shojaie, " An Efficient ADMM Algorithm for Structural Break Detection in Multivariate Time Series," NIPS Time Series Workshop, December 2017.
Y.-A. Ma, N. Foti, and E.B. Fox, "Stochastic Gradient MCMC Methods for Hidden Markov Models," International Conference in Machine Learning (ICML), Sydney, Australia August 2017.
C. Aicher and E.B. Fox, "Scalable Clustering of Correlated Time Series using Expectation Propagation," KDD Workshop on Mining and Learning from Time Series, August 2016.
Y.-A. Ma, T. Chen, and E.B. Fox, "A Complete Recipe for Stochastic Gradient MCMC," Neural Information Processing Systems (NIPS), Montreal, Quebec December 2015.
N. Foti, J. Xu, D. Laird, and E.B. Fox, "Stochastic Variational Inference for Hidden Markov Models," Neural Information Processing Systems (NIPS), Montreal, Quebec December 2014.
This material is based upon work supported by the National Science Foundation under Grant No. 1350133. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.