Forecasting Simultaneously High-Dimensional Time Series: A Robust Model-Based Clustering Approach

Y. Wang, R.S. Tsay, Johannes Ledolter, K.M Shrestha

Publication: Scientific journalJournal articlepeer-review

Abstract

This paper considers the problem of forecasting high-dimensional time series. It employs a robust clustering approach to perform classification of the component series. Each series within a cluster is assumed to follow the same model and the data are then pooled for estimation. The classification is model-based and robust to outlier contamination. The robustness is achieved by using the intrinsic mode functions of the Hilbert-Huang transform at lower frequencies. These functions are found to be robust to outlier contamination. The paper also compares out-of-sample forecast performance of the proposed method with several methods available in the literature. The other forecasting methods considered include vector autoregressive models with ∕ without LASSO, group LASSO, principal component regression, and partial least squares. The proposed method is found to perform well in out-of-sample forecasting of the monthly unemployment rates of 50 US states.
Original languageEnglish
Pages (from-to)673 - 684
JournalJournal of Forecasting
Volume32
Issue number8
DOIs
Publication statusPublished - 1 Dec 2013

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