A Model for Non-Stationary Time Series and its Applications in Filtering and Anomaly Detection

Time series measurements from sensing units (e.g., UWB ranging circuits) always suffer from uncertainties like noises, outliers, dropouts, and/or nonspecific anomalies. In order to extract the true information with high precision from the original corrupted measurements, the signal-model-based signa...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2021, Vol.70, p.1-11
Main Authors: Wang, Shixiong, Li, Chongshou, Lim, Andrew
Format: Article
Language:English
Subjects:
Anomalies
Anomaly detection
change point detection
Circuits
Data analysis
denoising
Dropouts
Integrated circuit modeling
Mathematical model
Noise measurement
Noise reduction
outlier correction
Outliers (statistics)
Polynomials
Sensors
signal modeling
Signal processing
Stochastic processes
Time measurement
Time series
Time series analysis
time-domain measurement
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Summary:Time series measurements from sensing units (e.g., UWB ranging circuits) always suffer from uncertainties like noises, outliers, dropouts, and/or nonspecific anomalies. In order to extract the true information with high precision from the original corrupted measurements, the signal-model-based signal pre-processing units embedded in sensing circuits are usually employed. However, for a general signal to observe, its signal model cannot be obtained so that the signal-model-based signal processing methods are not applicable. In this article, the time-variant local autocorrelated polynomial (TVLAP) model in the state space is proposed to model the dynamics of a non-stationary stochastic process (i.e., a signal or a time series), through which the model-based signal processing methods could be utilized to denoise, to correct the outliers/dropouts, and/or to identify anomalies contained in the measurements. Besides, the presented method can also be used in change point detection for a time series.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2021.3059321