Forecastable Component Analysis (ForeCA)
Methodology
2013-05-07 v3 Machine Learning
Abstract
I introduce Forecastable Component Analysis (ForeCA), a novel dimension reduction technique for temporally dependent signals. Based on a new forecastability measure, ForeCA finds an optimal transformation to separate a multivariate time series into a forecastable and an orthogonal white noise space. I present a converging algorithm with a fast eigenvector solution. Applications to financial and macro-economic time series show that ForeCA can successfully discover informative structure, which can be used for forecasting as well as classification. The R package ForeCA (http://cran.r-project.org/web/packages/ForeCA/index.html) accompanies this work and is publicly available on CRAN.
Keywords
Cite
@article{arxiv.1205.4591,
title = {Forecastable Component Analysis (ForeCA)},
author = {Georg M. Goerg},
journal= {arXiv preprint arXiv:1205.4591},
year = {2013}
}
Comments
10 pages, 4 figures; ICML 2013