Greedy Gaussian segmentation of multivariate time series

DOI10.1007/s11634-018-0335-0zbMath1459.62170arXiv1610.07435OpenAlexW2963460797WikidataQ129351037 ScholiaQ129351037MaRDI QIDQ2324258

Peter Nystrup, David Hallac, Stephen P. Boyd

Publication date: 11 September 2019

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1610.07435

zbMATH Keywords

time series analysisgreedy algorithmschange-point detectioncovariance regularizationtext segmentationfinancial regimes

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Related Items (5)

An $L_0$-Norm Regularized Method for Multivariate Time Series Segmentation ⋮ Change-Point Detection for Graphical Models in the Presence of Missing Values ⋮ Sparse group fused Lasso for model segmentation: a hybrid approach ⋮ Forecasting market states ⋮ A Total Variation Based Method for Multivariate Time Series Segmentation

Uses Software

• scout
• GitHub
• rarhsmm

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