Multivariate time series
Evolutionary multivariate time series prediction
... Multivariate time series (MTS) prediction plays a significant role in many practical data mining applications, such as finance, energy supply, and medical care ...one time series) are ...
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Optimization in semi-supervised classification of multivariate time series
... using time series distance measures and PU-learning have an upper hand compared to non-temporal OCC models in semi-supervised time series ...that multivariate time series ...
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Discovery of Temporal Association Rules in Multivariate Time Series
... of time series make temporal association rule mining ...over multivariate time series, which is proved to be scalable in terms of the number of time series and the ...
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A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data
... Nowadays, multivariate time series data are increasingly col- lected in various real world systems, ...variate time series refer to identifying abnormal status in cer- tain time ...
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Multivariate time series classification based on M-histograms and multi-view learning
... Concerning the Mgram model, it has been used to improve image con- trast [19]. This paper describes variations of the used of histograms to deter- mine threshold allowing image contrast enhancement. We also find ...
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Bayesian Multivariate Time Series Methods for Empirical Macroeconomics
... with multivariate time series models such as VARs, factor augmented VARs as well as time- varying parameter versions of these models (including variants with mul- tivariate stochastic ...and ...
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On the Efficiency of Outlier Generating Mechanisms in Multivariate Time Series
... in multivariate time series, [12] made use of projection pursuit technique while [3] proposed the Independent Component Analysis (ICA) as a tool capable of identifying the locations of multiple ...
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Inputs Selection for Artificial Neural Networks for Multivariate time Series
... for multivariate time series is ...output time series are analyzed and suitable mathematical models are built in the input-output model parametric ...furnace multivariate ...
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Testing independence for multivariate time series via the auto distance correlation matrix
... matrix multivariate auto-distance covariance and correlation functions for time series, dis- cuss their interpretation and develop consistent estimators for practical ...for multivariate ...
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On Multivariate Time Series Model Selection Involving Many Candidate VAR Models
... considered as the best estimate of the unknown true model. However, difficulty may arise in VAR model selection when the number of the candidate models is extremely large. Specifi- cally, assuming that some of the ...
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A similarity of multivariate time series in stocks network analysis
... This is because, in daily stock market activity, the price information for each stock is recorded in opening, highest, lowest, and closing prices. Thus, stock is a multivariate time series of those ...
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Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting
... model multivariate time-series data entirely in the spectral ...structural multivariate inputs into spectral time-series representations, while different trends can be decomposed ...
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An Efficient Bayesian Approach to Multiple Structural Change in Multivariate Time Series
... Multivariate time series data plays a central role in macroeconomic analysis and ...univariate time series. Existing multivariate change- point models have restrictions on the ...
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Efficient and Scalable Techniques for Multivariate Time Series Analysis and Search
... Multivariate time series (MTS) data mining presents major challenges and, a fair amount of pre-processing is often required to improve the usability of the data for downstream pattern recognition ...
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Copula-based semiparametric models for multivariate time series
... for multivariate time series, serial dependence is either ignored or treated separately from ...individual series are typically modeled first, and a copula is used to capture the dependence ...
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Meta-heuristic Methods for Outliers Detection in Multivariate Time Series
... used projection pursuit techniques while the latter em- ploys independent component analysis (ICA); (Barbi- eri, 1991) used a Bayesian method and finally a graphi- cal method was explored by (Khattree and Naik, 1987). In ...
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An Empirical Comparison of Distance Measures for Multivariate Time Series Clustering
... how multivariate time series are far from each other ...either multivariate time series is close to some other multivariate time series samples, then the ...
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Research on dynamic time warping multivariate time series similarity matching based on shape feature and inclination angle
... Multivariate time series model representation From the TD method of Lee et ...the time maintaining length were changed, the accuracy rate would be changed ...of time maintaining length ...
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Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains
... of multivariate time series where measurements in dif- ferent channels are taken from the different parts of the sys- ...in time series analysis [14, 15, ...
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Locally Adaptive Factor Processes for Multivariate Time Series
... modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance ...certain time intervals exhibiting rapid changes and others in which ...
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