This paper aims at testing and modeling business-cycle asymmetries within a structural time-series framework, allowing for smooth transition in the parameters characterizing the cyclical component, namely, the damping factor and the frequency. An LM test of linearity is derived, and illustrations are provided with reference to a set of quarterly U.S. industrial production series for two-digit m...

Pathological tremors exhibit a nonlinear oscillation that is not strictly periodic. We investigate whether the deviation from periodicity is due to nonlinear deterministic chaotic dynamics or due to nonlinear stochastic dynamics. To do so, we apply various methods from linear and nonlinear time series analysis to tremor time series. The results of the different methods suggest that the consider...

earthquakes show unknown nonlinear behavior and given the magnitude of the earthquake, we would encounter certain changes in lithosphere, atmosphere and ionosphere. the ionospheric parameters have been found to be sorely susceptible to major earthquakes. in addition to the ionospheric variations generated by solar activity, there are remarkable temporary changes in the ionosphere that are gener...

We show that a class of microeconomic behavioral models with interacting agents, derived from Kirman (1991) and Kirman (1993), can replicate the empirical long-memory properties of the two first-conditional moments of financial time series. The essence of these models is that the forecasts and thus the desired trades of the individuals in the markets are influenced, directly or indirectly, by t...

Recently, it has been proposed a new method to investigate the linear/nonlinear properties of a time series by decomposing it into two sub-series: magnitude and sign sub-series (Ashkenazy et al., 2003). The long-range correlation properties of the magnitude and sign sub-series relate to nonlinear and linear behavior of the original time series respectively. We apply this method to the hourly ti...

Identifying causal relations among simultaneously acquired signals is an important problem in multivariate time series analysis. For linear stochastic systems Granger proposed a simple procedure called the Granger causality to detect such relations. In this work we consider nonlinear extensions of Granger’s idea and refer to the result as Extended Granger Causality. A simple approach implementi...

The goal of this chapter is to examine whether the time series of returns of securities listed on Taiwan’s stock exchange exhibit significant nonlinear serial dependencies, as do those of other stock exchanges throughout the world, and, if so, to examine the pervasiveness of such dependencies across the individual stocks trading on the Taiwan Stock Exchange. First, an examination of the charact...

While the disciplines of physics and engineering sciences in many cases have taken advantage from accurate time-series prediction by applying ordinary differential equation systems upon precise basic physical laws such approach hardly could be adopted by other scientific disciplines where precise mathematical basic laws are unknown. A new modelling schema, the NMPC-graph, opens the possibility ...

Improvement of time series forecasting accuracy is an active research area having significant importance in many practical domains. Extensive works in literature suggest that substantial enhancement in accuracies can be achieved by combining forecasts from different models. However, forecasts combination is a difficult as well as a challenging task due to various reasons and often simple linear...

This review covers the group of data-analysis techniques collectively referred to as symbolization or symbolic time-series analysis. Symbolization involves transformation of raw time-series measurements (i.e., experimental signals) into a series of discretized symbols that are processed to extract information about the generating process. In many cases, the degree of discretization can be quite...