Mining latent sources of causal time series using nonlinear state space modeling

Authors:
Wei-Shing Chen;Fong-Jung Yu
Affiliations:
Department of Industrial Engineering and Technology Management, Taiwan, R.O.C;Department of Industrial Engineering and Technology Management, Taiwan, R.O.C
Venue:
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Year:
2011

Citing 10
Cited 0

Bayesian forecasting and dynamic models (2nd ed.)

Bayesian forecasting and dynamic models (2nd ed.)
Linear System Theory and Design

Linear System Theory and Design
An unsupervised ensemble learning method for nonlinear dynamic state-space models

Neural Computation
Chaos and Time-Series Analysis

Chaos and Time-Series Analysis
Estimating parameters and hidden variables in non-linear state-space models based on ODEs for biological networks inference

Bioinformatics
A data mining framework for time series estimation

Journal of Biomedical Informatics
Determining number of independent sources in undercomplete mixture

EURASIP Journal on Advances in Signal Processing
Estimating the Number of Hidden Neurons of the MLP Using Singular Value Decomposition and Principal Components Analysis: A Novel Approach

SBRN '10 Proceedings of the 2010 Eleventh Brazilian Symposium on Neural Networks
Inferring the eigenvalues of covariance matrices from limited,noisy data

IEEE Transactions on Signal Processing
Nonlinear dynamical factor analysis for state change detection

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Data mining refers to use of new methods for the intelligent analysis of large data sets. This paper applies one of nonlinear state space modeling (NSSM) techniques named nonlinear dynamical factor analysis (NDFA) to mine the latent factors which are the original sources for producing the observations of causal time series. The purpose of mining indirect sources rather than the time series observation is that much better results can be obtained from the latent sources, for example, economics data driven by an "explanatory variables" like inflation, unobserved trends and fluctuations. The effectiveness of NDFA is evaluated by a simulated time series data set. Our empirical study indicates the performance of NDFA is better than the independent component analysis in exploring the latent sources of Taiwan unemployment rate time series.