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Invariant time-series factorization

Josif Grabocka, Lars Schmidt-Thieme
2014 Data mining and knowledge discovery  
Time-series classification is an important domain of machine learning and a plethora of methods have been developed for the task.  ...  The time-series are projected to a new feature representation consisting of the sums of the membership weights, which captures frequencies of local patterns.  ...  Factorization of time series There have been a few attempts in generating invariant time-series features through factorization.  ... 
doi:10.1007/s10618-014-0364-z fatcat:x3uooabvz5ejxknabg2qq7bkxm

Analyzing Invariants in Cyber-Physical Systems using Latent Factor Regression

Marjan Momtazpour, Jinghe Zhang, Saifur Rahman, Ratnesh Sharma, Naren Ramakrishnan
2015 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15  
We describe a latent factor approach to infer invariants underlying system variables and how we can leverage these relationships to monitor a cyber-physical system.  ...  The analysis of large scale data logged from complex cyber-physical systems, such as microgrids, often entails the discovery of invariants capturing functional as well as operational relationships underlying  ...  When the dependency between two time series does not change over time, we say that these two time series are system-invariants. DEFINITION 2.  ... 
doi:10.1145/2783258.2788605 dblp:conf/kdd/MomtazpourZRSR15 fatcat:4dhd4n45cvdhxi3szxqif2s4ey

Learning Disentangled Representations for Time Series [article]

Yuening Li, Zhengzhang Chen, Daochen Zha, Mengnan Du, Denghui Zhang, Haifeng Chen, Xia Hu
2021 arXiv   pre-print
Time-series representation learning is a fundamental task for time-series analysis.  ...  To bridge the gap, we propose Disentangle Time Series (DTS), a novel disentanglement enhancement framework for sequential data.  ...  (b) Semantic factors of time-series, where a semantic factor controls the sequential trend of a time-series. Figure 1 : 1 Two traversal plot examples of disentanglement.  ... 
arXiv:2105.08179v2 fatcat:dyvhspxhtfd55mdi2kktfzlasi

Making the Dynamic Time Warping Distance Warping-Invariant [article]

Brijnesh Jain
2019 arXiv   pre-print
The literature postulates that the dynamic time warping (dtw) distance can cope with temporal variations but stores and processes time series in a form as if the dtw-distance cannot cope with such variations  ...  To eliminate these peculiarities, we convert the dtw-distance to a warping-invariant semi-metric, called time-warp-invariant (twi) distance.  ...  To close the gap of the warping-invariance problem, we convert the dtw-distance to a warping-invariant semi-metric, called time-warp-invariant (twi) distance.  ... 
arXiv:1903.01454v2 fatcat:q53oy56zabftpbnyob3r4hj3r4

Connected to TV series: Quantifying series watching engagement

István Tóth-Király, Beáta Bőthe, Eszter Tóth-Fáber, Győző Hága, Gábor Orosz
2017 Journal of Behavioral Addictions  
In Study 2 (N = 944), measurement invariance of the SWES was investigated between males and females.  ...  Methods: In Study 1 (N Sample1 = 740 and N Sample2 = 740), exploratory structural equation modeling and confirmatory factor analysis were used to identify the most important facets of series watching engagement  ...  High levels of invariance (invariance of factor loadings, intercepts, error variances, factor variances, factor covariances, and factor means) were demonstrated across gender groups, supporting the generalizability  ... 
doi:10.1556/2006.6.2017.083 pmid:29280396 pmcid:PMC6034953 fatcat:td6jvkgtebcd7ljagox7l6svr4

Page 207 of Journal of Family Psychology Vol. 28, Issue 2 [page]

2014 Journal of Family Psychology  
across time.  ...  First, we estimate a series of different CFGM implying strong invariance. We start with a level-only model. We then estimate a linear trajectory model with a level and a slope factor.  ... 

Longitudinal Multi-Trait-State-Method Model Using Ordinal Data

R. Shane Hutton, Sy-Miin Chow
2014 Multivariate Behavioral Research  
over time, and a second-order factor analytic model to capture shared trait variances among the state affect factors.  ...  change over time.  ...  The loadings of the trait factors on all state factors are constrained to be invariant across time to reflect the time-invariant nature of the trait factors.  ... 
doi:10.1080/00273171.2014.903832 pmid:26735192 fatcat:t3j3oswqkzbl7cy3bnagt2ptli

Measurement invariance within and between individuals: a distinct problem in testing the equivalence of intra- and inter-individual model structures

Janne Adolf, Noémi K. Schuurman, Peter Borkenau, Denny Borsboom, Conor V. Dolan
2014 Frontiers in Psychology  
., a time series model with latent variables. Implemented in a multiple-occasion and multiple-subject setting, the model simultaneously accounts for intra-individual and inter-individual differences.  ...  We consider the conditions-in terms of invariance constraints-under which modeling results are generalizable (a) over time within subjects, (b) over subjects within occasions, and (c) over time and subjects  ...  First, we based our modeling on the linear, time-invariant Kalman filter and ML estimation which led to time-invariant time series models.  ... 
doi:10.3389/fpsyg.2014.00883 pmid:25346701 pmcid:PMC4193237 fatcat:f4uy6y2k3zegdie4sd7kq2wrpy

A Square-Root Second-Order Extended Kalman Filtering Approach for Estimating Smoothly Time-Varying Parameters [article]

Zachary F. Fisher, Sy-Miin Chow, Peter C. M. Molenaar, Barbara L. Fredrickson, Vladas Pipiras, Kathleen M. Gates
2020 arXiv   pre-print
We examine the performance of our approach in a Monte Carlo simulation and show the proposed algorithm accurately recovers the unobserved states in the case of a bivariate dynamic factor model with time-varying  ...  This approach is capable of handling dynamic factor models where the relations between variables underlying the processes of interest change in a manner that may be difficult to specify in advance.  ...  Time-Invariant Table 2 : 2 Mean Percentage of Relative Bias for Parameters Generated as Time-Invariant Time Series Length T = 70 T = 200 T = 500 Simulation Condition Simulation Condition  ... 
arXiv:2007.09672v1 fatcat:h53zrgd5kfbibmoen7fbeqzue4

On decoding of DVR-based linear network codes

Qifu Tyler Sun, Shuo-Yen Robert Li
2015 Applicable Algebra in Engineering, Communication and Computing  
An existing time-invariant decoding algorithm is at a delay equal to the largest valuation among all invariant factors of the received submodule.  ...  Meanwhile, time-variant decoding is formulated. The meaning of time-invariant decoding delay gets a new interpretation through being a special case of the time-variant counterpart.  ...  Then, a time-invariant decoding matrix exists with a decoding delay no more than the largest valuation among invariant factors of the submodule f e : e ∈ In(v) in D ω .  ... 
doi:10.1007/s00200-015-0264-5 fatcat:5kve7j4feremzboieossmotjmu


2000 Fractals  
This scale-invariance cannot be accounted for by current theoretical models, and resembles some of the scenarios described for self-organized criticality.  ...  Figure 3(B) illustrates an important consequence of scale-invariance: on increasing the observation time by a factor of k, the fluctuations are found to be k α larger.  ...  way to some common external factor.  ... 
doi:10.1142/s0218348x00000329 fatcat:2wlepaliozfntgx2or7xah3z6y

A Complexity-Invariant Distance Measure for Time Series [chapter]

Gustavo E.A.P.A. Batista, Xiaoyue Wang, Eamonn J. Keogh
2011 Proceedings of the 2011 SIAM International Conference on Data Mining  
The ubiquity of time series data across almost all human endeavors has produced a great interest in time series data mining in the last decade.  ...  We introduce the first complexity-invariant distance measure for time series, and show that it generally produces significant improvements in classification accuracy.  ...  The Euclidean distance, ED(Q,C), between two time series Q and C, can be made complexity-invariant by introducing a correction factor: ( , ) = ( , ) × ( , ) Where CF is a complexity correction factor defined  ... 
doi:10.1137/1.9781611972818.60 dblp:conf/sdm/BatistaWK11 fatcat:37ccsipr2jf7djxyphlfokdgui

Large Scale-Invariant Fluctuations in Normal Blood Cell Counts: A sign of criticality? [article]

Carlos A. Perazzo, Elmer A. Fernandez, Dante R. Chialvo, Peter Willshaw
2000 arXiv   pre-print
This scale-invariance cannot be accounted for by current theoretical models, and resembles some of the scenarios described for self-organized criticality.  ...  Panel b in Figure 3 illustrates an important consequence of scale-invariance: on increasing the observation time by a factor of k the fluctuations are found to be k α larger, that is to say the longer  ...  way to some common external factor.  ... 
arXiv:physics/0005029v1 fatcat:4hjbnmlplre55odibnxiwowvuq

Exploring Finite-Sized Scale Invariance in Stochastic Variability with Toy Models: The Ornstein–Uhlenbeck Model

Nachiketa Chakraborty
2020 Symmetry  
This finite size of the scale invariance depends upon the decay time in the OU model.  ...  Quantifying stochastic properties of observed time-series or lightcurves, can provide insights into the underlying physical mechanisms driving variability, especially those of the particles that radiate  ...  And self-similarity refers to the scale invariance of the time-series.  ... 
doi:10.3390/sym12111927 fatcat:cgrxdcdv2vbzrpg4danue22z3m

Person-Oriented and Subject-Specific Methodology: Some Additional Remarks

Peter C. M Molenaar
2016 Journal for Person-Oriented Research  
Objections that dynamic factor analysis, a prime subject-specific variable-oriented method, enables testing of all central person-oriented theoretical principles are answered in principled ways and a conjecture  ...  In Molenaar (2015b) a generalized methodology for testing factor invariance is introduced according to which factor series in dynamic factor models with time-varying loadings can have identical (time-invariant  ...  Millsap, 2011) the latent factor series in a dynamic factor model with time-varying factor loadings therefore appear to have time-varying meaning and therefore are incomparable across time.  ... 
doi:10.17505/jpor.2016.03 fatcat:fh23jqnn5bcztoma3kltetry7a
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