.png){kind=icon}
Return
Convolutional PCA for Multiple Time Series
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2020.3016185.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We study a fundamental generalization of principal component analysis (PCA) that looks for a small set of common time series, i.e., principal components (PCs), whose filtered versions can explain most of the variances of multiple observed time series. This problem boils down to PCA in the frequency domain, in principle. But, frequency domain processing suffers from aliasing, and brings inconveniences when handling certain time domain properties like nonstationarity, and sparsity. We propose novel time, and z-domain costs for such PCA, and study its properties in detail with setting of either finite or diverging filter lengths. We further discuss its implementations, and possible extensions, and present numerical results for empirical performance study. Convolution is used to extract these PCs, thus the name convolutional PCA.
Keywords:
Principal component analysis
Frequency-domain analysis
Time series analysis
Convolution
Time-domain analysis
Spatiotemporal phenomena
Indexes
Principal component analysis (PCA)
time series
second order statistics
convolution
signal detection
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W
Organization
No organization information available
Cited Papers
ShareSavePRINCIPAL COMPONENT ANALYSIS FOR SECOND-ORDER STATIONARY VECTOR TIME SERIES.png)
ANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
OAAIShareSaveShareSaveAnsa-[(2,2-bis-η-cyclopentadienyl)propane]-molybdenum and -tungsten chemistry and related studies
PREAI
ShareSaveAnion- and pH-linked effects on the heme-iron geometry in ferrous nitrosylated monomeric myoglobins
PREAI
ShareSave
ShareSaveShareSave no more
no more