Forecasting simultaneously high-dimensional time series: a robust model-based clustering approach

Yongning Wang, Ruey S. Tsay, Johannes Ledolter, Keshab M. Shrestha

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8 Citations (Scopus)


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 â̂ 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
Number of pages12
JournalJournal of Forecasting
Issue number8
Publication statusPublished - Dec 2013
Externally publishedYes


  • Hilbert-Huang transform
  • LASSO regression
  • Markov chain Monte Carlo
  • model-based clustering
  • partial least squares
  • principal component regression

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