Learning algorithms have become an integral component to modern engineering solutions.
Examples range from self-driving cars and recommender systems to finance and even …

Vector autoregressive moving-average (VARMA) processes are suitable models for
producing linear forecasts of sets of time series variables. They provide parsimonious …

When I worked on my Introduction to Multiple Time Series Analysis (Lutk ̈ ̈-pohl (1991)), a
suitable textbook for this? eld was not available. Given the great importance these methods …

In this paper, we analyze the finite sample complexity of stochastic system identification
using modern tools from machine learning and statistics. An unknown discrete-time linear …

Granger causality has long been a prominent method for inferring causal interactions
between stochastic variables for a broad range of complex physical systems. However, it …

the mathematics of financial modeling & investment management The Mathematics of
Financial Modeling & Investment Management covers a wide range of technical topics in …

In this paper, we consider the task of designing a Kalman Filter (KF) for an unknown and
partially observed autonomous linear time invariant system driven by process and sensor …

The state-space approach provides a formal framework where any result or procedure
developed for a basic model can be seamlessly applied to a standard formulation written in …

Before introducing heuristic optimization methods and providing an overview of some
applications in econometrics, we have to motivate the use of such an optimization paradigm …

When I worked on my Introduction to Multiple Time Series Analysis (Lütkepohl (1991)), a
suitable textbook for this field was not available. Given the great importance these methods …