Journal
IEEE SIGNAL PROCESSING LETTERS
Volume 19, Issue 10, Pages 611-614Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2012.2209641
Keywords
Dimensionality reduction; granger causality; time-series prediction
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Funding
- Seoul National University of Science Technology
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We deal with the problem of time-series prediction in a dyadic setup where the goal is to predict future values of the output sequence from the observed input sequence. Often the input time-series data is high-dimensional with potential noisy measurements included, which can make the prediction task difficult. In this paper, we propose a novel dimensionality reduction algorithm that can sparsely extract most salient and discriminative input features for output prediction. Our approach is based on the Granger causality, a famous statistical technique particularly in economics, where we aim to discover a low-dimensional subspace that preserves the causality between input and output. We demonstrate empirically the benefits of the proposed approaches on several datasets.
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