Model specification and selection for multivariate time series

Specification Testing for Multivariate Time Series Volatility Models

Volatility models have been playing an important role in economics and finance. Using a multivariate generalized spectral approach, we propose a new class of generally applicable omnibus tests for univariate and multivariate volatility models. Both GARCH models and stochastic volatility models are covered. Our tests have a convenient asymptotic null N(0,1) distribution, and can detect a wide ra...

Decomposability and selection of graphical models for multivariate time series

A Supervised Feature Subset Selection Technique for Multivariate Time Series

Feature subset selection (FSS) is a known technique to pre-process the data before performing any data mining tasks, e.g., classification and clustering. FSS provides both cost-effective predictors and a better understanding of the underlying process that generated data. We propose Corona, a simple yet effective supervised feature subset selection technique for Multivariate Time Series (MTS). T...

DECADE: A Deep Metric Learning Model for Multivariate Time Series

Determining similarities (or distance) between multivariate time series sequences is a fundamental problem in time series analysis. The complex temporal dependencies and variable lengths of time series make it an extremely challenging task. Most existing work either rely on heuristics which lacks flexibility and theoretical justifications, or build complex algorithms that are not scalable to bi...

Modelling Multivariate Time Series

Multivariate time series (MTS) data are widely available in di erent elds including medicine, nance, science and engineering. Modelling MTS data e ectively is important for many decision-making activities. In this paper, we will describe some of our e orts in modelling these data for numerous tasks such as outlier analysis, forecasting and explanation. Through the analysis of various kinds of M...