Forecasting Multiple Time Series With One-Sided Dynamic Principal Components

@article{Pea2019ForecastingMT,
  title={Forecasting Multiple Time Series With One-Sided Dynamic Principal Components},
  author={Daniel Pe{\~n}a and Ezequiel Smucler and Victor J. Yohai},
  journal={Journal of the American Statistical Association},
  year={2019},
  volume={114},
  pages={1683 - 1694}
}
Abstract We define one-sided dynamic principal components (ODPC) for time series as linear combinations of the present and past values of the series that minimize the reconstruction mean squared error. Usually dynamic principal components have been defined as functions of past and future values of the series and therefore they are not appropriate for forecasting purposes. On the contrary, it is shown that the ODPC introduced in this article can be successfully used for forecasting high… 

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