In this paper, a concise survey of the literature on cyclostationarity is presented and includes
an extensive bibliography. The literature in all languages, in which a substantial amount of …

Seasonality is an important feature of essentially all natural systems but the consequences
of seasonality have been vastly underappreciated. Early work emphasized the role of …

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 …

Multivariate simultaneous equations models were used extensively for macroeconometric
analysis when Sims (1980) advocated vector autoregressive (VAR) models as alternatives …

This book offers an in-depth and up-to-date review of different statistical tools that can be
used to analyze and forecast the dynamics of two crucial for every energy company …

We introduce Mixed Data Sampling (henceforth MIDAS) regression models. The regressions
involve time series data sampled at different frequencies. Technically speaking MIDAS …

This book contains a up-to-date overview of nonlinear time series models and their
application to modelling economic relationships. It considers nonlinear models in stationary …

Random forest (RF) regression is an extremely popular tool for analyzing high-dimensional
data. Nonetheless, its benefits may be lessened in sparse settings due to weak predictors …

The advantages of periodic control have been known since humanity learned to cultivate
crops in rotation to increase production. In more recent times, it has been recognized that …

This study aims to predict the direction of US stock prices by integrating time-varying
effective transfer entropy (ETE) and various machine learning algorithms. At first, we explore …