Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Component extraction analysis of multivariate time series
Date
1996-05-01
Author
Akman, I
DeGooijer, JG
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
180
views
0
downloads
Cite This
A method for modelling several observed parallel time series is proposed. The method involves seeking possible common underlying pure AR and MA components in the series. The common components are forced to be mutually uncorrelated so that univariate time series modelling and forecasting techniques can be applied. The proposed method is shown to be a useful addition to the time series analyst's toolkit, if common sources of variation in multivariate data need to be quickly identified.
Subject Keywords
Components extraction
,
Multivariate time series
,
Nonstationarity
,
Nonstationarity
URI
https://hdl.handle.net/11511/65320
Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
DOI
https://doi.org/10.1016/0167-9473(95)00031-3
Collections
Department of Computer Engineering, Article
Suggestions
OpenMETU
Core
Time series classification with feature covariance matrices
Ergezer, Hamza; Leblebicioğlu, Mehmet Kemal (2018-06-01)
In this work, a novel approach utilizing feature covariance matrices is proposed for time series classification. In order to adapt the feature covariance matrices into time series classification problem, a feature vector is defined for each point in a time series. The feature vector comprises local and global information such as value, derivative, rank, deviation from the mean, the time index of the point and cumulative sum up to the point. Extracted feature vectors for the time instances are concatenated t...
Time Series Classification Using Point-wise Features
Ergezer, Hamza; Leblebicioğlu, Mehmet Kemal (2017-05-18)
In this work, a novel approach utilizing feature covariance matrices is proposed for time series classification. In order to adapt the feature covariance matrices for time series classification, a feature vector is defined for each point in a time series. The feature vector comprises local and global information such as value, derivative, rank, deviation from the mean, time index of the point and cumulative sum up to the point. Instead of representing the whole time series with a single covariance matrix, t...
Particle Filtering with Propagation Delayed Measurements
Orguner, Umut (2010-03-13)
This paper investigates the problem of propagation delayed measurements in a particle filtering scenario. Based on implicit constraints specified by target dynamics and physics rules of signal propagation, authors apply the ideas that were first proposed in their previous work to the case of particle filters. Unlike the deterministic sampling based approach called propagation delayed measurement filter (PDMF) in their previous work, the new algorithm proposed here (called as PDM particle filter (PDM-PF)) ha...
Piecewise-planar 3D reconstruction in rate-distortion sense
Imre, Evren; Gueduekbay, Ugur; Alatan, Abdullah Aydın (2007-05-09)
In this paper, a novel rate-distortion optimization inspired 3D piecewise-planar reconstruction algorithm is proposed. The algorithm refines a coarse 3D triangular mesh, by inserting vertices in a way to minimize the intensity difference between an image and its prediction. The preliminary experiments on synthetic and real data indicate the validity of the proposed approach.
Continuous-time nonlinear estimation filters using UKF-aided gaussian sum representations
Gökçe, Murat; Kuzuoğlu, Mustafa; Department of Electrical and Electronics Engineering (2014)
A nonlinear filtering method is developed for continuous-time nonlinear systems with observations/measurements carried out in discrete-time by means of UKFaided Gaussian sum representations. The time evolution of the probability density function (pdf) of the state variables (or the a priori pdf) is approximated by solving the Fokker-Planck equation numerically using Euler’s method. At every Euler step, the values of the a priori pdf are evaluated at deterministic sample points. These values are used with Ga...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
I. Akman and J. DeGooijer, “Component extraction analysis of multivariate time series,”
COMPUTATIONAL STATISTICS & DATA ANALYSIS
, pp. 487–499, 1996, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/65320.