Changepoint Analysis in Time Series Data: Past and PresentSpecial Events
|Speaker:||Xueheng Shi, Univ. Nebraska-Lincoln (Stat)|
|Start time:||Fri, Dec 2 2022, 2:05PM|
Abrupt structural changes (changepoints) arise in many scenarios, for example, mean/trend shifts in time series, coefficient changes in the regressions. Changepoint analysis plays an important role in modelling and prediction of time series, and has vast applications in finance, climatology, signal processing and so on. This talk reviews prominent algorithms (Binary Segmentation/Wild Binary Segmentation and Pruned Exact Linear Time (PELT)) to detect mean shifts in time series. However, these methods require IID model errors while the time series are often autocorrelated (serial dependence). Changepoint analysis under serial dependence is a well-known difficult problem. We propose a gradient-descent dynamic programming algorithm to find the changepoints in time series data.
This research is joint work with Dr. Gallagher (Clemson University), Dr. Killick (Lancaster University, UK) and Dr. Lund (UCSC).
This is a part of the first Joint CeDAR/UCD4IDS Conference.