Multivariate time series may contain outliers of different types. In the presence of such outliers, applying standard multivariate time series techniques becomes unreliable. A robust version of multivariate exponential smoothing is proposed. The method is affine equivariant, and involves the selection of a smoothing parameter matrix by minimizing a robust loss function. It is shown that the robust method results in much better forecasts than the classic approach in the presence of outliers, and performs similarly when the data contain no outliers. Moreover, the robust procedure yields an estimator of the smoothing parameter less subject to downward bias. As a byproduct, a cleaned version of the time series is obtained, as is illustrated by means of a real data example.
Croux, C., Gelper, S. E. C., & Mahieu, K. (2010). Robust exponential smoothing of multivariate time series. Computational Statistics and Data Analysis, 54(12), 2999-3006. https://doi.org/10.1016/j.csda.2009.05.003