Generalized Framework for Similarity Measure of Time Series

Yin, Hongsheng; Qi, Honggang; Xu, Jingwen; Hung, William N. N.; Song, Xiaoyu

doi:10.1155/2014/572124

Cited by 4 publications

(5 citation statements)

References 16 publications

(25 reference statements)

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“…In this section, we first describe representative baseline methods and three datasets for stock indices, environment, and traffic. Subsequently, the parameters and evaluation indicators corresponding to the model were identified, and the most appropriate window size w for each data set was determined according to formula (1). Finally, we compare the proposed model against the other four different baseline methods.…”

Section: Simulation Experiments and Analysis Of Resultsmentioning

confidence: 99%

“…A complex system often requires multiple variables to be described, and people can obtain the values of these variables from different angles on the same object at different times, thus forming the multivariate time series (MTS). 1 The matrix ⋯ X X X X = ( , , , )…”

Section: Introductionmentioning

confidence: 99%

boldX = false({boldX}_{1}, {boldX}_{2}, ⋯, {boldX}_{m} false)

can be described as a mathematical representation of the MTS whose samples are usually denoted as

x_{i} false(t false) false(i = 1, 2, ⋯, n; t = 1, 2, ⋯, m false)

, where

n

denotes the number of variables and

m

denotes the total length of time.…”

Section: Introductionmentioning

confidence: 99%

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A dual‐stage attention‐based Conv‐LSTM network for spatio‐temporal correlation and multivariate time series prediction

et al. 2021

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Multivariate time series (MTS) prediction aims at predicting future time series by extracting multiple forms of dependencies of past time series. Traditional prediction methods and deep learning‐based prediction methods focus on extracting the dynamic relationships of certain aspects of MTS, especially the temporal characteristics, often neglecting the spatial and temporal dynamic correlations of MTS. Inspired by convolution neural network (CNN) and attention mechanism, this paper proposes a convolution LSTM network model based on MTS prediction with two‐stage attention. Specifically, we first propose a new MTS preprocessing method to perform convolution operations better. Then convolution layer extracts spatial correlation of MTS and LSTM model extracts temporal correlation. It is worth mentioning that the combination of attention mechanism and LSTM can effectively solve the problem of insufficient time dependency in MTS prediction. In addition, dual‐stage attention mechanism can effectively eliminate irrelevant information, select the relevant exogenous sequence, give it higher weight, and increase the past value of the target sequence to further eliminate irrelevant information. Finally, the MTS spatio‐temporal correlation is extracted to improve the prediction accuracy, and the model is interpreted. Experimental results show that the model has broad application prospects. Experiments based on typical datasets of finance, environment, and energy determine the optimal window size and hidden size of the prediction, and demonstrate that the model achieves the state‐of‐the‐art effect compared to the other four deep learning models. On top of that, the model is not only suitable for single‐step prediction of MTS, but also suitable for multistep prediction of time step in a certain range.

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Section: Simulation Experiments and Analysis Of Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

boldX = false({boldX}_{1}, {boldX}_{2}, ⋯, {boldX}_{m} false)

can be described as a mathematical representation of the MTS whose samples are usually denoted as

x_{i} false(t false) false(i = 1, 2, ⋯, n; t = 1, 2, ⋯, m false)

, where

n

denotes the number of variables and

m

denotes the total length of time.…”

Section: Introductionmentioning

confidence: 99%

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A dual‐stage attention‐based Conv‐LSTM network for spatio‐temporal correlation and multivariate time series prediction

et al. 2021

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“…period length). Selecting one or more appropriate measures is not trivial (G lynn et al 2006; R efinetti et al 2007; D ing et al 2008; B atista et al 2011; J in 2011; S un et al 2014; Y in et al 2014; B anko and A bonyi 2015; K otsifakos et al 2016; M ori et al 2016). This is particularly true when comparing time series with differently calibrated, non-linear scales that may be associated with substantial measurement errors, as is often the case for experimental observations.…”

Section: Modelsmentioning

confidence: 99%

FlyClockbase: Importance of Biological Model Curation for Analyzing Variability in the Circadian Clock of Drosophila melanogaster by Integrating Time Series from 25 Years of Research

Scheuer

Hanlon

Dresel

et al. 2017

Preprint

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General Article SummarySubstantial biological model curation was essential for identifying differences in peak 23 time variance of the clock-proteins 'PERIOD' and 'TIMELESS', which probably stem 24 from differences in phosphorylation-network complexity. 25 We repeatedly encountered systemic limitations of contemporary data analysis 26 strategies in our work on circadian clocks. Thus, we used it as an opportunity for 27 composing a panoramic view of the broader challenges in biological model curation, 28 which are likely to increase as biologists aim to integrate all existing expertise in order to 29 address diverse grand challenges. We developed and tested a trans-disciplinary 30 research workflow, which enables biologists and compiler-architects to define biology-31 friendly compilers for efficiently constructing and maintaining Versioned Biological 32 Information Resources (VBIRs). We report insights gleaned from our practical clock 33 research that are essential for defining a VBIRs infrastructure, which improves the 34 efficiency of biological model curation to the point where it can be democratized. . CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/099192 doi: bioRxiv preprint first posted online Jan. 9, 2017;. CC-BY 4.0 International license It ...

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“…Time series pattern matching can measure similarity of univariate or multivariate data [14], 2 Mathematical Problems in Engineering which provides a possibility for the coupling assessment of single or integrated disturbance. Common pattern matching methods for univariate time series are Euclidean distance (ED) and dynamic time warping (DTW) distance.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Modeling Strategy for Smart Grid Power Quality Coupling Assessment Based on Time Series Pattern Matching

Jia

Wang

et al. 2018

Mathematical Problems in Engineering

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This study introduces a data-driven modeling strategy for smart grid power quality (PQ) coupling assessment based on time series pattern matching to quantify the influence of single and integrated disturbance among nodes in different pollution patterns. Periodic and random PQ patterns are constructed by using multidimensional frequency-domain decomposition for all disturbances. A multidimensional piecewise linear representation based on local extreme points is proposed to extract the patterns features of single and integrated disturbance in consideration of disturbance variation trend and severity. A feature distance of pattern (FDP) is developed to implement pattern matching on univariate PQ time series (UPQTS) and multivariate PQ time series (MPQTS) to quantify the influence of single and integrated disturbance among nodes in the pollution patterns. Case studies on a 14-bus distribution system are performed and analyzed; the accuracy and applicability of the FDP in the smart grid PQ coupling assessment are verified by comparing with other time series pattern matching methods.

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Generalized Framework for Similarity Measure of Time Series

Cited by 4 publications

References 16 publications

A dual‐stage attention‐based Conv‐LSTM network for spatio‐temporal correlation and multivariate time series prediction

A dual‐stage attention‐based Conv‐LSTM network for spatio‐temporal correlation and multivariate time series prediction

FlyClockbase: Importance of Biological Model Curation for Analyzing Variability in the Circadian Clock of Drosophila melanogaster by Integrating Time Series from 25 Years of Research

A Data-Driven Modeling Strategy for Smart Grid Power Quality Coupling Assessment Based on Time Series Pattern Matching

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