Accurate Changing Point Detection for Mean Filtering

It is often desirable to find the underlying trends in time series data. This is a well known signal processing problem that has many applications in areas such as financial data analysis, climatology, biological and medical sciences. Mean filtering finds a piece-wise constant trend in the data whil...

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Bibliographic Details
Published in:IEEE signal processing letters Vol. 23; no. 2; pp. 297 - 301
Main Authors: Ottersten, Johan, Wahlberg, Bo, Rojas, Cristian R.
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Data models
Difference equations
l1 mean filtering
Noise measurement
Signal processing
Signal processing algorithms
stair-case effect
taut-string algorithm
Time series
Time series analysis
Trends
stair-case effect
taut-string algorithm
{\ell _1} mean filtering
ISSN:1070-9908, 1558-2361
Online Access:Get full text
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Summary:It is often desirable to find the underlying trends in time series data. This is a well known signal processing problem that has many applications in areas such as financial data analysis, climatology, biological and medical sciences. Mean filtering finds a piece-wise constant trend in the data while trend filtering finds a piece-wise linear trend. When the signal is noisy, the main difficulty is finding the changing points in the data that mark the transition points when the mean or the trend changes. Previously proposed methods based on ℓ 1 filtering suffer from the occurrence of false changing points in the estimate. This is known as the stair-case effect. The main contribution in this paper is incorporating a technique to remove these false changing points to a fast mean filtering algorithm, referred to as the taut-string method, resulting in an efficient procedure with accurate change point detection and thus the removal of the stair-case effect.
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ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2016.2517605