Contrastive Multivariate Singular Spectrum Analysis
We introduce Contrastive Multivariate Singular Spectrum Analysis, a novel unsupervised method for dimensionality reduction and signal decomposition of time series data. By utilizing an appropriate background dataset, the method transforms a target time series dataset in a way that evinces the sub-signals that are enhanced in the target dataset, as opposed to only those that account for the greatest variance. This shifts the goal from finding signals that explain the most variance to signals that matter the most to the analyst. We demonstrate our method on an illustrative synthetic example, as well as show the utility of our method in the downstream clustering of electrocardiogram signals from the public MHEALTH dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringDimensionality ReductionTime SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
Multivariate Functional Singular Spectrum Analysis Over Different Dimensional Domains
In this work, we develop multivariate functional singular spectrum analysis (MFSSA) over different dimensional domains which is the functional extension of multivariate singular spectrum analysis (MSSA). In the following…
Time SeriesTime Series AnalysisUnderstanding fluctuations through Multivariate Circulant Singular Spectrum Analysis
We introduce Multivariate Circulant Singular Spectrum Analysis (M-CiSSA) to provide a comprehensive framework to analyze fluctuations, extracting the underlying components of a set of time series, disentangling their sou…
Time SeriesTime Series AnalysisChange Point Detection via Multivariate Singular Spectrum Analysis
The objective of change point detection (CPD) is to detect significant and abrupt changes in the dynamics of the underlying system of interest through multivariate time series observations. In this work, we develop and a…
Change Point DetectionTime SeriesTime Series AnalysisTime-series image denoising of pressure-sensitive paint data by projected multivariate singular spectrum analysis
Time-series data, such as unsteady pressure-sensitive paint (PSP) measurement data, may contain a significant amount of random noise. Thus, in this study, we investigated a noise-reduction method that combines multivaria…
DenoisingImage DenoisingTime SeriesTime Series AnalysisOn Multivariate Singular Spectrum Analysis and its Variants
We introduce and analyze a variant of multivariate singular spectrum analysis (mSSA), a popular time series method to impute and forecast a multivariate time series. Under a spatio-temporal factor model we introduce, giv…
ImputationTime SeriesTime Series AnalysisTime Series Prediction