TY  - JOUR
A1  - Schütz, Nadine
A1  - Holschneider, Matthias
T1  - Detection of trend changes in time series using Bayesian inference
T2  - Physical review : E, Statistical, nonlinear and soft matter physics
N2  - Change points in time series are perceived as isolated singularities where two regular trends of a given signal do not match. The detection of such transitions is of fundamental interest for the understanding of the system's internal dynamics or external forcings. In practice observational noise makes it difficult to detect such change points in time series. In this work we elaborate on a Bayesian algorithm to estimate the location of the singularities and to quantify their credibility. We validate the performance and sensitivity of our inference method by estimating change points of synthetic data sets. As an application we use our algorithm to analyze the annual flow volume of the Nile River at Aswan from 1871 to 1970, where we confirm a well-established significant transition point within the time series.
Y1  - 2011
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/36689
SN  - 1539-3755
SN  - 1550-2376
VL  - 84
IS  - 2
PB  - American Physical Society
CY  - College Park
ER  -