Time Series Analysis and Forecasting: Selected Contributions by Ignacio Rojas, Héctor Pomares

By Ignacio Rojas, Héctor Pomares

This quantity provides chosen peer-reviewed contributions from The overseas Work-Conference on Time sequence, ITISE 2015, held in Granada, Spain, July 1-3, 2015. It discusses themes in time sequence research and forecasting, complicated equipment and on-line studying in time sequence, high-dimensional and complex/big info time sequence in addition to forecasting in actual problems.

The overseas Work-Conferences on Time sequence (ITISE) offer a discussion board for scientists, engineers, educators and scholars to debate the most recent principles and implementations within the foundations, concept, versions and functions within the box of time sequence research and forecasting. It makes a speciality of interdisciplinary and multidisciplinary learn encompassing the disciplines of desktop technology, arithmetic, data and econometrics.

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E (2016, submitted) Threshold Autoregressive Models for Directional Time Series Mahayaudin M. Mansor, Max E. Glonek, David A. Green, and Andrew V. Metcalfe Abstract Many time series show directionality as plots against time and against time-to-go are qualitatively different. A stationary linear model with Gaussian noise is non-directional (reversible). Directionality can be emulated by introducing non-Gaussian errors or by using a nonlinear model. Established measures of directionality are reviewed and modified for time series that are symmetrical about the time axis.

Glonek, David A. Green, and Andrew V. Metcalfe Abstract Many time series show directionality as plots against time and against time-to-go are qualitatively different. A stationary linear model with Gaussian noise is non-directional (reversible). Directionality can be emulated by introducing non-Gaussian errors or by using a nonlinear model. Established measures of directionality are reviewed and modified for time series that are symmetrical about the time axis. The sunspot time series is shown to be directional with relatively sharp increases.

Mansor, Max E. Glonek, David A. Green, and Andrew V. Metcalfe Abstract Many time series show directionality as plots against time and against time-to-go are qualitatively different. A stationary linear model with Gaussian noise is non-directional (reversible). Directionality can be emulated by introducing non-Gaussian errors or by using a nonlinear model. Established measures of directionality are reviewed and modified for time series that are symmetrical about the time axis. The sunspot time series is shown to be directional with relatively sharp increases.

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