Big data permeates business, engineering, and science — the number of connected smart devices, even excluding phones, tablets, and PCs, is projected to grow from billions to tens of billions within five years. Vast data is generated from sensors, GPS, RFID, medical devices, and emergency and energy systems, to provide rich information about untold aspects of the modern world. Despite the ubiquity and significant interest in mining such data, there are few existing analytical tools that are suitable.
We develop new high dimensional and functional time series (HDTS) tools to help researchers and practitioners meet increasingly ambitious inferential and modeling aims. This includes: (i) new methods for simultaneous modeling, inference, and forecasting of dynamic functional data; (ii) new structured regularization methods for modeling high dimensional time ordered data; (iii) new adaptive, yet interpretable methods of stability analysis for big data monitoring systems; and (iv) linking these new methods with emergent lines of inquiry and providing an infrastructure for answering critical research questions in a wide range of fields.
Tupper, L., Matteson, D.S., and Anderson, C.L. (2015), “Band Depth Clustering for Nonstationary Time Series and Wind Speed Behavior.”
Nicholson, W.B., Matteson, D.S. and Bien, J. (2015), “VARX-L: Structured Regularization for Large Vector Autoregressions with Exogenous Variables.”
Nicholson, W.B., Bien, J. and Matteson, D.S. (2015), “HVAR: High Dimensional Forecasting via Interpretable Vector Autoregression.”
Kowal, D.R., Matteson, D.S. and Ruppert, D. (2015), “A Bayesian Multivariate Functional Dynamic Linear Model.”
Holan, S.H., Yang, W.-H., Matteson, D.S. and Wikle, C.K. (2012), “An Approach for Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Functional Models,” Applied Stochastic Models in Business and Industry, Vol. 28, No. 6, 485-499.
Holan, S.H., Yang, W.-H., Matteson, D.S. and Wikle, C.K. (2012), “Rejoinder, An Approach for Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Functional Models,” Applied Stochastic Models in Business and Industry, Vol. 28, No. 6, 504-505.
Matteson, D.S., McLean, M.W., Woodard, D.B. and Henderson, S.G. (2011), “Forecasting Emergency Medical Service Call Arrival Rates,” Annals of Applied Statistics, Vol. 5, No. 2B, 1379-1406.
Matteson, D.S. and Tsay, R.S. (2011), “Dynamic Orthogonal Components for Multivariate Time Series,” Journal of the American Statistical Association, Vol. 106, No. 496, 1450-1463.
Matteson, D.S. and Tsay, R.S. (2007), “High Dimensional Volatility Models,” JSM Proceedings, Business and Economics Statistics Section, Alexandria, VA: American Statistical Association, 1006-1013.
- R Package: bigVAR – Dimension Reduction Methods for Multivariate Time Series (2014)
- Nicholson, W.B., Bien, J. and Matteson, D.S.