Time Series

Friday, April 27, 2018 - 9:30am - 10:00am
Sumanta Basu (Cornell University)
The problem of learning interactions among the components of a large system from time series data is becoming increasingly common in many areas of biological and social sciences. Examples include learning regulatory interactions from time course gene expression data, understanding policy implications from a large number of macroeconomic time series, risk management and monitoring of financial institutions and exploring functional connections among different regions of human brain.
Thursday, April 26, 2018 - 1:30pm - 2:00pm
Daniel Kowal (Rice University)
We propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building upon the global-local framework of prior construction, in which continuous scale mixtures of Gaussian distributions are employed for both desirable shrinkage properties and computational tractability, we allow the local scale parameters to depend on the history of the shrinkage process.
Thursday, April 26, 2018 - 10:30am - 11:00am
Garvesh Raskutti (University of Wisconsin, Madison)
Consider a multi-variate time series, which may correspond to spike train responses for multiple neurons in a brain, crime event data across multiple regions, and many others. An important challenge associated with these time series models is to estimate an influence network between the d variables, especially when the number of variables d is large meaning we are in the high-dimensional setting. Prior work has focused on parametric vector auto-regressive models. However, parametric approaches are somewhat restrictive in practice.
Wednesday, February 21, 2018 - 8:30am - 9:10am
Mark Fiecas (University of Minnesota, Twin Cities)
In this talk, we will give an overview of statistical methodologies for spectral analysis of time series data. We will briefly discuss the common approaches for spectral analysis, and discuss their limitations for analyzing data whenever the study has a longitudinal experimental design. To address the limitations, we propose a Bayesian model for spectral analysis that accounts for the covariation within a subject.
Wednesday, September 7, 2011 - 10:45am - 11:25am
Zhaohua Wu (Florida State University)
Determining trend and implementing detrending operations are important steps in data analysis. Traditionally, various extrinsic methods have been used to determine the trend, and to facilitate a detrending operation. In this talk, a simple and logical definition of trend is given for any nonlinear and non-stationary time series as an intrinsically determined monotonic function within a certain temporal span (most often that of the data span), or a function in which there can be at most one extremum within that temporal span.
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