Time series: theory and methods
Time series: theory and methods
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Bayesian multiscale analysis for time series data
Computational Statistics & Data Analysis
Wavelet based time-varying vector autoregressive modelling
Computational Statistics & Data Analysis
Visualization and inference based on wavelet coefficients, SiZer and SiNos
Computational Statistics & Data Analysis
Detecting change-points in Markov chains
Computational Statistics & Data Analysis
Modelling the US, UK and Japanese unemployment rates: Fractional integration and structural breaks
Computational Statistics & Data Analysis
The mobile fitness coach: Towards individualized skill assessment using personalized mobile devices
Pervasive and Mobile Computing
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Identifying short and long range change points in an observed time series that consists of stationary segments is a common problem. These change points mark the time boundaries of the segments where the time series leaves one stationary state and enters another. Due to certain technical advantages, analysis is carried out in the frequency domain to identify such change points in the time domain. What is considered as a change may depend on the time scale. The results of the analysis are displayed in the form of graphs that display change points on different time horizons (time scales), which are observed to be statistically significant. The methodology is illustrated using several simulated and real time series data. The method works well to detect change points and illustrates the importance of analysing the time series on different time horizons.