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.Comment: 10 pages, 4 figures; ICML 201