Time series segmentation: A sliding window approach

Chu, Chia-Shang James

doi:10.1016/0020-0255(95)00021-g

Cited by 71 publications

(32 citation statements)

References 20 publications

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“…The segmentation problem is: given a time series, T, partition T into segments (windows) that are internally homogeneous with respect to the application. [2]; String Matching (SM) [2]; Reference-based Windowing (RbW) [6]; Dynamic Windowing (DWin) [7]; and Variablesize Sliding Window (VSW) [8]. The sliding window techniques (FNSW, FOSW and SWAB) are said to be online capable, while ToD and BUp are not online capable.…”

Section: Signal Segmentation Techniquesmentioning

confidence: 99%

On time series sensor data segmentation for fall and activity classification

Achumba

Bersch

Khusainov

et al. 2012

2012 IEEE 14th International Conference on E-Health Networking, Applications and Services (Healthcom)

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The vast amount of literature on human ambulation and Activities of Daily Living (ADL) events classification has highlighted significant details on most aspects of the research area including: monitoring techniques, Wearable Sensor-based Monitoring Device (WSMD) placement on human body parts, and ambulation and ADL data collection methods, among others. However literature has failed to highlight meaningful details on one of the most important aspects of such studies, sensor data segmentation for feature extraction. The choice of segmentation techniques is in general very important, because inappropriate segmentation will most likely result in features without discriminant power. No classifier of whatever sophistication will give meaningful results with features that have no discriminant power. The optimal segmentation technique has been empirically investigated using sensor data from a bi-axial accelerometer. Results of the empirical investigation are presented.

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Section: Signal Segmentation Techniquesmentioning

confidence: 99%

On time series sensor data segmentation for fall and activity classification

Achumba

Bersch

Khusainov

et al. 2012

2012 IEEE 14th International Conference on E-Health Networking, Applications and Services (Healthcom)

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“…Two methods are used for analysis of an irregularly sampled data; namely, direct value interpolation [35][36][37][38][39] and windows-based segmentation [40,41]. The former assumes that all values are collected regularly with a pre-specified sampling frequency and converts time series with irregular observations to discrete time observation sequences.…”

Section: Handling Irregularly Sampled Datamentioning

confidence: 99%

A Temporal Mining Framework for Classifying Un-Evenly Spaced Clinical Data

2016

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“…Instead of values at pre-specified regularly sampled time points, the approach first segments time series to fixed-sized windows. The behavior in the window is summarized in terms of its statistics γ , such as, the mean, or the last value observed within that time interval [19–24]. The values generated by the different windows define sequences of γ statistics.…”

Section: Modeling Clinical Time Seriesmentioning

confidence: 99%

Clinical time series prediction: Toward a hierarchical dynamical system framework

Liu

Hauskrecht

2015

Artificial Intelligence in Medicine

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Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance.

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Time series segmentation: A sliding window approach

Cited by 71 publications

References 20 publications

On time series sensor data segmentation for fall and activity classification

On time series sensor data segmentation for fall and activity classification

A Temporal Mining Framework for Classifying Un-Evenly Spaced Clinical Data

Clinical time series prediction: Toward a hierarchical dynamical system framework

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