New independent component analysis tools for time series

2015 ◽  
Vol 105 ◽  
pp. 80-87 ◽  
Author(s):  
Markus Matilainen ◽  
Klaus Nordhausen ◽  
Hannu Oja
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Analysis Tools

Outliers Detection in Multivariate Time Series by Independent Component Analysis

2007 ◽  
Vol 19 (7) ◽  
pp. 1962-1984 ◽  
Author(s):  
Roberto Baragona ◽  
Francesco Battaglia
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Model Building ◽  
Multivariate Time Series ◽  
Component Analysis ◽  
Independent Component ◽  
Level Shifts ◽  
The Common ◽  
Identification Techniques ◽  
Pattern Occurrences

In multivariate time series, outlying data may be often observed that do not fit the common pattern. Occurrences of outliers are unpredictable events that may severely distort the analysis of the multivariate time series. For instance, model building, seasonality assessment, and forecasting may be seriously affected by undetected outliers. The structure dependence of the multivariate time series gives rise to the well-known smearing and masking phenomena that prevent using most outliers' identification techniques. It may be noticed, however, that a convenient way for representing multiple outliers consists of superimposing a deterministic disturbance to a gaussian multivariate time series. Then outliers may be modeled as nongaussian time series components. Independent component analysis is a recently developed tool that is likely to be able to extract possible outlier patterns. In practice, independent component analysis may be used to analyze multivariate observable time series and separate regular and outlying unobservable components. In the factor models framework too, it is shown that independent component analysis is a useful tool for detection of outliers in multivariate time series. Some algorithms that perform independent component analysis are compared. It has been found that all algorithms are effective in detecting various types of outliers, such as patches, level shifts, and isolated outliers, even at the beginning or the end of the stretch of observations. Also, there is no appreciable difference in the ability of different algorithms to display the outlying observations pattern.

PATTERN RECOGNITION OF THE TERM STRUCTURE USING INDEPENDENT COMPONENT ANALYSIS

2006 ◽  
Vol 20 (02) ◽  
pp. 173-188 ◽  
Author(s):  
EDMOND HAOCUN WU ◽  
PHILIP L. H. YU
Keyword(s):  
Time Series ◽  
Pattern Recognition ◽  
Independent Component Analysis ◽  
Term Structure ◽  
Financial Time Series ◽  
Empirical Studies ◽  
Multivariate Time Series ◽  
Principal Component ◽  
Component Analysis ◽  
Independent Component

Term structure is a useful curve describing some financial asset as a function of time to maturity or expiration. In this paper, we propose to use Independent Component Analysis (ICA) to model the term structure of multiple yield curves. The idea is that we first employ ICA to decompose the multivariate time series, then we suggest two ICA methods for dimension reduction and pattern recognition of the term structure. We also compare the results by using an alternative method, Principal Component Analysis (PCA). The empirical studies suggest that the proposed ICA approaches outperform PCA methods in modeling the term structure. This model can be used in financial time series analysis as well as related financial applications.

VALUE AT RISK ESTIMATION USING INDEPENDENT COMPONENT ANALYSIS-GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSCEDASTICITY (ICA-GARCH) MODELS

2006 ◽  
Vol 16 (05) ◽  
pp. 371-382 ◽  
Author(s):  
EDMOND H. C. WU ◽  
PHILIP L. H. YU ◽  
W. K. LI
Keyword(s):  
At Risk ◽  
Time Series ◽  
Independent Component Analysis ◽  
Value At Risk ◽  
Risk Estimation ◽  
Multivariate Time Series ◽  
Component Analysis ◽  
Independent Component ◽  
Garch Models ◽  
Computationally Efficient

We suggest using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series. Then, we propose to use ICA-GARCH models which are computationally efficient to estimate the multivariate volatilities. The experimental results show that the ICA-GARCH models are more effective than existing methods, including DCC, PCA-GARCH, and EWMA. We also apply the proposed models to compute value at risk (VaR) for risk management applications. The backtesting and the out-of-sample tests validate the performance of ICA-GARCH models for value at risk estimation.

scholarly journals Spatial independent component analysis of functional MRI time-series: To what extent do results depend on the algorithm used?

2002 ◽  
Vol 16 (3) ◽  
pp. 146-157 ◽  
Author(s):  
Fabrizio Esposito ◽  
Elia Formisano ◽  
Erich Seifritz ◽  
Rainer Goebel ◽  
Renato Morrone ◽  
...  
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Functional Mri ◽  
Component Analysis ◽  
Independent Component
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