Forecastable Component Analysis (ForeCA)

TL;DR

ForeCA is a new dimension reduction method for time series that separates forecastable components from noise, aiding forecasting and classification tasks.

Contribution

It introduces a novel forecastability measure and an efficient algorithm for component separation in multivariate time series.

Findings

Successfully applied to financial and macro-economic data

Discoveres informative structures for forecasting

Provides a publicly available R package

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.