Introduced more than a half-century ago, Granger causality has become a popular tool for
analyzing time series data in many application domains, from economics and finance to …

In this paper, we survey the most recent advances in supervised machine learning (ML) and
high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear …

While most classical approaches to Granger causality detection assume linear dynamics,
many interactions in real-world applications, like neuroscience and genomics, are inherently …

Classifying forecasting methods as being either of a “machine learning” or “statistical” nature
has become commonplace in parts of the forecasting literature and community, as …

This is an expository paper that reviews recent developments on optimal estimation of
structured high-dimensional covariance and precision matrices. Minimax rates of …

We model a large panel of time series as a vector autoregression where the autoregressive
matrices and the inverse covariance matrix of the system innovations are assumed to be …

Many applications collect a large number of time series, for example, the financial data of
companies quoted in a stock exchange, the health care data of all patients that visit the …

The vector autoregression (VAR) has long proven to be an effective method for modeling the
joint dynamics of macroeconomic time series, as well as for forecasting. One major …

Identification of the parameters of stable linear dynamical systems is a well-studied problem
in the literature, both in the low and high-dimensionalsettings. However, there are hardly any …

Vector autoregression (VAR) is a fundamental tool for modeling multivariate time series.
However, as the number of component series is increased, the VAR model becomes …