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 …

This paper surveys recent developments related to the smooth transition autoregressive
(STAR) time series model and several of its variants. We put emphasis on new methods for …

In this paper we propose a flexible model to describe nonlinearities and long-range
dependence in time series dynamics. The new model is a multiple regime smooth transition …

Forecasting of time series that have seasonal and other variations remains an important
problem for forecasters. This paper presents a neural network (NN) approach to forecasting …

This paper explores the time variation in the stock–bond correlation using high-frequency
data. Gradual transitions between regimes of negative and positive stock–bond correlation …

We propose, in this paper, a framework for time series and nonlinear system modeling,
called the basis function matrix-based flexible coefficient autoregressive (BFM-FCAR) …

We propose a gradient radial basis function based varying-coefficient autoregressive (GRBF-
AR) model for modeling and predicting time series that exhibit nonlinearity and …

When assessing the predictive power of financial variables for economic activity,
researchers usually aggregate higher-frequency data before estimating a forecasting model …

In this paper we provide an alternative approach to analyze the demand for international
tourism in the Balearic Islands, Spain, by using a neural network model that incorporates …

In this paper, we propose a fully Bayesian approach to the special class of nonlinear time‐
series models called the logistic smooth transition autoregressive (LSTAR) model. Initially, a …