Independent Component Analysis for Clustering Multivariate Time Series Data

2005 ◽  
pp. 474-482 ◽  
Author(s):  
Edmond H. C. Wu ◽  
Philip L. H. Yu
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Component Analysis ◽  
Independent Component ◽  
Series Data

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 On Independent Component Analysis with Stochastic Volatility Models

2017 ◽  
Vol 46 (3-4) ◽  
pp. 57-66 ◽  
Author(s):  
Markus Matilainen ◽  
Jari Miettinen ◽  
Klaus Nordhausen ◽  
Hannu Oja ◽  
Sara Taskinen
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Stochastic Volatility ◽  
Financial Time Series ◽  
Multivariate Time Series ◽  
Component Analysis ◽  
Independent Component ◽  
Stationary Time Series ◽  
Volatility Models ◽  
High Volatility

Consider a multivariate time series where each component series is assumed to be a linear mixture of latent mutually independent stationary time series. Classical independent component analysis (ICA) tools, such as fastICA, are often used to extract latent series, but they don't utilize any information on temporal dependence. Also financial time series often have periods of low and high volatility. In such settings second order source separation methods, such as SOBI, fail. We review here some classical methods used for time series with stochastic volatility, and suggest modifications of them by proposing a family of vSOBI estimators. These estimators use different nonlinearity functions to capture nonlinear autocorrelation of the time series and extract the independent components. Simulation study shows that the proposed method outperforms the existing methods when latent components follow GARCH and SV models. This paper is an invited extended version of the paper presented at the CDAM 2016 conference.

Exploratory Analysis of Fossil-Fuel CO2 Emissions Time Series Using Independent Component Analysis

2011 ◽  
pp. 1520-1538
Author(s):  
Sargam Parmar ◽  
Bhuvan Unhelkar
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Co2 Emissions ◽  
Fossil Fuel ◽  
Multivariate Time Series ◽  
Component Analysis ◽  
Linear Mapping ◽  
Independent Component ◽  
Carbon Dioxide Co2 ◽  
Ica Algorithm

Carbon dioxide (CO2) is one of the most important gases in the atmosphere, and is necessary for sustaining life on Earth. However, it is also a major greenhouse gas out of the six that contribute to global warming and climate change. During the last decade technologists, economists and sociologists are taking substantial interest in studying the impact of greenhouse phenomenon. Scientists are trying to find solutions to reduce CO2 emissions by changes in structure of energy production and consumption. Every attempt is being made to use new models and methods to estimate measure and monitor greenhouse gases in the future. Independent Component Analysis (ICA) is a method for automatically identifying a set of underlying factors in a given data set. This chapter describes the use of the ICA algorithm in Environmentally Intelligent (EI) applications. EI applications have a wide ranging responsibilities including collection, analysis and reporting of environmental data related to the organization. ICA algorithm opens up the opportunity to improve the quality of data being analyzed by these EI applications. ICA finds application in several fields of interest and it is a tempting alternative to try ICA on multivariate time series such as a CO2 emission from fossil fuel for the period 1950 to 2006. This chapter describes the linear mapping of the observed multivariate time series into a new space of statistically independent components (ICs) that might reveal driving mechanisms for CO2 emissions that may otherwise remain hidden.

Exploratory Analysis of Fossil-Fuel CO2 Emissions Time Series Using Independent Component Analysis

2011 ◽  
pp. 385-403
Author(s):  
Sargam Parmar ◽  
Bhuvan Unhelkar
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Co2 Emissions ◽  
Fossil Fuel ◽  
Multivariate Time Series ◽  
Component Analysis ◽  
Linear Mapping ◽  
Independent Component ◽  
Carbon Dioxide Co2 ◽  
Ica Algorithm

Carbon dioxide (CO2) is one of the most important gases in the atmosphere, and is necessary for sustaining life on Earth. However, it is also a major greenhouse gas out of the six that contribute to global warming and climate change. During the last decade technologists, economists and sociologists are taking substantial interest in studying the impact of greenhouse phenomenon. Scientists are trying to find solutions to reduce CO2 emissions by changes in structure of energy production and consumption. Every attempt is being made to use new models and methods to estimate measure and monitor greenhouse gases in the future. Independent Component Analysis (ICA) is a method for automatically identifying a set of underlying factors in a given data set. This chapter describes the use of the ICA algorithm in Environmentally Intelligent (EI) applications. EI applications have a wide ranging responsibilities including collection, analysis and reporting of environmental data related to the organization. ICA algorithm opens up the opportunity to improve the quality of data being analyzed by these EI applications. ICA finds application in several fields of interest and it is a tempting alternative to try ICA on multivariate time series such as a CO2 emission from fossil fuel for the period 1950 to 2006. This chapter describes the linear mapping of the observed multivariate time series into a new space of statistically independent components (ICs) that might reveal driving mechanisms for CO2 emissions that may otherwise remain hidden.

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