Forecastable Component Analysis (ForeCA)
This method addresses the challenge of analyzing high-dimensional time series data for researchers and practitioners in fields like finance and economics, offering a new tool for dimension reduction and forecasting.
The authors tackled the problem of dimension reduction for temporally dependent signals by introducing Forecastable Component Analysis (ForeCA), a novel technique that separates multivariate time series into forecastable and white noise components, with applications in financial and macro-economic data showing successful discovery of informative structure for forecasting and classification.
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.