From Data to Knowledge - 108 - Jeff Scargle
Channel: ckleinastro
Date: 2012-05-08

Slides: http://lyra.berkeley.edu/CDIConf/pdfs/streaming_talk_scarg

Jeff Scargle: "Bayesian Blocks: Segmented Models for Detecting and Characterizing Transients in Streaming Time Series". A video from the UC Berkeley Conference: From Data to Knowledge: Machine-Learning with Real-time and Streaming Applications (May 7-11, 2012).

Abstract
Jeff Scargle (NASA Ames, Planetary Systems, Astrobiology and Space Science)
Bayesian Blocks finds the best fitting piece-wise constant model of time series data. This algorithm uses dynamic programming to find the exact global optimum over all possible partitions of N data points (an exponentially large search space!) in time of order N**2. Recent elaborations of the basic method include: treatment of gaps and exposure variations in general, calibration of the parameter in the prior on the number of blocks, multivariate analysis including mixed data modes, solution of the empty block problem, piece-wise linear and piecewise exponential models, and partitioning data on the circle. All of these features can be implemented in a real-time mode in which the algorithm triggers on the first change-point found in the data stream, as well as in the standard retrospective mode.