Generalized feature extraction for structural pattern recognition in time-series data

By Robert T. Olszewski, Roy A. Maxion, D. Siewiorek. 2001

340 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d74fa52a7ddd41b175378dbe0604e635dab8a708

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

A Survey on Human Activity Recognition using Wearable Sensors (2013, background): “In general, two approaches have been proposed to extract features from time series data: statistical and structural [50].”
Semi-supervised time series classification (2006, methodology): “The wafer dataset is a collection of time series containing a sequence of measurements recorded by one vacuum-chamber sensor during the etch process of silicon wafers for semiconductor fabrication [33].”
Functional data clustering: a survey (2013, methodology): “3 This dataset consists of 200 electrocardiograms from 2 groups of patients sampled at 96 time instants, and has already been studied in Olszewski (2001).”
Data Augmentation techniques in time series domain: a survey and taxonomy (2022, methodology): “It was proposed in [70], where a fixed slice window was defined from which the data is rearranged, but it has also been applied with variable windows, as it was done in [79] using ECG data at 300 Hz.”
Rotation-invariant similarity in time series using bag-of-patterns representation (2012, background): “…oversight, as the structural approach is particularly useful for long time series, or for applications where domain experts compare time series or signals based on the arrangement of morphological events present in the signals (Olszewski 2001), e.g., radar signal detection and speech recognition.”
Multivariate analysis of variance for functional data (2017, methodology): “Information on the time series used is presented in Table 1 [5,28,31,37].”