Name
Abstract | ||
|---|---|---|
Change point analysis is a statistical tool to identify homogeneity within time series data. We propose a pruning approach for approximate nonparametric estimation of multiple change points. This general purpose change point detection procedure 'cp3o' applies a pruning routine within a dynamic program to greatly reduce the search space and computational costs. Existing goodness-of-fit change point objectives can immediately be utilized within the framework. We further propose novel change point algorithms by applying cp3o to two popular nonparametric goodness of fit measures: 'e-cp3o' uses E-statistics, and 'ks-cp3o' uses Kolmogorov-Smirnov statistics. Simulation studies highlight the performance of these algorithms in comparison with parametric and other nonparametric change point methods. Finally, we illustrate these approaches with climatological and financial applications. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICDMW.2017.44 | 2017 IEEE International Conference on Data Mining Workshops (ICDMW) |
Keywords | Field | DocType |
Dynamic programming,Pruning | Econometrics,Time series,Change detection,Homogeneity (statistics),Change-Point Analysis,Nonparametric statistics,Parametric statistics,Statistics,Goodness of fit,Mathematics,Pruning | Conference |
ISSN | ISBN | Citations |
2375-9232 | 978-1-5386-3801-9 | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenyu Zhang | 1 | 165 | 26.47 |
| Nicholas A. James | 2 | 0 | 0.34 |
| David S. Matteson | 3 | 13 | 5.08 |