A similarity measure for temporal pattern discovery in time series data generated by IoT

2016 ◽  
Author(s):  
Shadi Aljawarneh ◽  
Vangipuram Radhakrishna ◽  
Puligadda Veereswara Kumar ◽  
Vinjamuri Janaki
Keyword(s):  
Time Series ◽  
Similarity Measure ◽  
Temporal Pattern ◽  
Time Series Data ◽  
Pattern Discovery ◽  
Series Data ◽  
Temporal Pattern Discovery

A Temporal Pattern Discovery Method and its Application to Physiological Data: A Case Study (Preprint)

2019 ◽  
Author(s):  
Catherine Inibhunu ◽  
Carolyn McGregor
Keyword(s):  
Time Series ◽  
Temporal Pattern ◽  
Clinical Decision Making ◽  
Time Series Data ◽  
Pattern Discovery ◽  
Mathematical Formulation ◽  
Series Data ◽  
Physiological Data ◽  
Time Period ◽  
Temporal Pattern Discovery

BACKGROUND High frequency data collected from monitors and sensors that provide measures relating to patients’ vital status in intensive care units (NICUs) has the potential to provide valuable insights which can be crucial when making critical decisions for the care of premature and ill term infants. However, this exercise is not trivial when faced with huge volumes of data that are captured every second at the bedside/home. The ability to collect, analyze and understand any hidden relationships in the data that may be vital for clinical decision making is a central challenge. OBJECTIVE The main goal of this research is to develop a method to detect and represent relationships that may exist in temporal abstractions (TA) and temporal patterns (TP) derived from time oriented data. The premise of this research is that in clinical care, the discovery of unknown relationships among physiological time oriented data can lead to detection of onset of conditions, aid in classifying abnormal or normal behaviors or derive patterns of an altered trajectory towards a problematic future state for a patient. That is, there is great potential to use this approach to uncover previously unknown pathophysiologies that are present in high speed physiological data. METHODS This research introduces a TPR process and an associated TPRMine algorithm which adopts a stepwise approach to temporal pattern discovery by first applying a scaled mathematical formulation of the time series data. This is achieved by modelling the problem space as a finite state machine representation where for a given timeframe, a time series data segment transitions from one state to another based on probabilistic weights and then quantifying the many paths a time series data may transition to. RESULTS The TPRMine Algorithm has been designed, implemented and applied to patient physiological data streams captured from the McMaster Children’s Hospital NICU. The algorithm has been applied to understand the number of states a patient in a NICU bed can transition to in a given time period and a demonstration of formulation of hypothesis tests. In addition, a quantification of these states is completed leading to creation of a vital scoring. With this, it’s possible to understand the percent of time a patient remains in a high or low vital score. CONCLUSIONS The developed method allows understanding the number of states a patient may transition to in any given time period. Adding some clinical context to the identified states facilitates state quantification allowing formulation of thresholds which leads to generating patient scores. This is an approach that can be utilized for identifying patient at risk of some clinical condition prior to disease progress. Additionally the developed method facilitates identification of frequent patterns that could be associated with generated thresholds.

Imputing incomplete time-series data based on varied-window similarity measure of data sequences

2007 ◽  
Vol 28 (9) ◽  
pp. 1091-1103 ◽  
Author(s):  
Sirapat Chiewchanwattana ◽  
Chidchanok Lursinsap ◽  
Chee-Hung Henry Chu
Keyword(s):  
Time Series ◽  
Similarity Measure ◽  
Time Series Data ◽  
Series Data

scholarly journals Fast Constrained Dynamic Time Warping for Similarity Measure of Time Series Data

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 222841-222858
Author(s):  
Wonyoung Choi ◽  
Jaechan Cho ◽  
Seongjoo Lee ◽  
Yunho Jung
Keyword(s):  
Time Series ◽  
Similarity Measure ◽  
Dynamic Time Warping ◽  
Time Series Data ◽  
Series Data ◽  
Time Warping ◽  
Dynamic Time

scholarly journals Pattern discovery in time series using autoencoder in comparison to nonlearning approaches

2021 ◽  
pp. 1-20
Author(s):  
Fabian Kai-Dietrich Noering ◽  
Yannik Schroeder ◽  
Konstantin Jonas ◽  
Frank Klawonn
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Pattern Discovery ◽  
Computing Time ◽  
Real Life ◽  
Synthetic Data ◽  
Series Data ◽  
Data Sets ◽  
Research Fields ◽  
The Matrix

In technical systems the analysis of similar situations is a promising technique to gain information about the system’s state, its health or wearing. Very often, situations cannot be defined but need to be discovered as recurrent patterns within time series data of the system under consideration. This paper addresses the assessment of different approaches to discover frequent variable-length patterns in time series. Because of the success of artificial neural networks (NN) in various research fields, a special issue of this work is the applicability of NNs to the problem of pattern discovery in time series. Therefore we applied and adapted a Convolutional Autoencoder and compared it to classical nonlearning approaches based on Dynamic Time Warping, based on time series discretization as well as based on the Matrix Profile. These nonlearning approaches have also been adapted, to fulfill our requirements like the discovery of potentially time scaled patterns from noisy time series. We showed the performance (quality, computing time, effort of parametrization) of those approaches in an extensive test with synthetic data sets. Additionally the transferability to other data sets is tested by using real life vehicle data. We demonstrated the ability of Convolutional Autoencoders to discover patterns in an unsupervised way. Furthermore the tests showed, that the Autoencoder is able to discover patterns with a similar quality like classical nonlearning approaches.

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