This paper generalises four types of disturbance commonly used in univariate time series analysis to the multivariate case, highlights the differences between univariate and multivariate outliers, and ...

We propose a method to specify an appropriate yet parsimonious vector autoregressive moving average (ARMA) model for a given multivariate time series. By considering con-temporaneous linear ...

Learn how to do time series regression using a neural network, with 'rolling window' data, coded from scratch, using Python.

Researchers analyze state-of-the-art approaches, limitations, and applications of deep learning-based anomaly detection in multivariate time series Monitoring financial security, industrial safety ...