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43.418 Articles

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Forecasting value-at-risk and expected shortfall for emerging markets using FIGARCH models

Alex Sandro Monteiro De Moraes,Antonio Carlos Figueiredo Pinto,Marcelo Cabus Klotzle

This paper compares the performance of long-memory models (FIGARCH) with short-memory models (GARCH) in forecasting volatility for calculating value-at-risk (VaR) and expected shortfall (ES) for multiple periods ahead for six emerging markets stock indice... see more

Expected shortfall | long-memory | volatility forecast | multiple steps ahead forecast | value-at-risk

Revista Brasileira de Finanças , 2015

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An Ensemble Empirical Mode Decomposition, Self-Organizing Map, and Linear Genetic Programming Approach for Forecasting River Streamflow

Jonathan T. Barge, Hatim O. Sharif

This study focused on employing Linear Genetic Programming (LGP), Ensemble Empirical Mode Decomposition (EEMD), and the Self-Organizing Map (SOM) in modeling the rainfall–runoff relationship in a mid-size catchment. Models were assessed with regard to the... see more

Water , 2016

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Exogenous Measurements from Basic Meteorological Stations for Wind Speed Forecasting

José Carlos Palomares-Salas, Agustín Agüera-Pérez, Juan José González de la Rosa, José María Sierra-Fernández and Antonio Moreno-Muñoz

This research presents a comparative analysis of wind speed forecasting methods applied to perform 1 h-ahead forecasting. The main significant development has been the introduction of low-quality measurements as exogenous information to improve these pred... see more

wind speed prediction | time series forecasting | artificial neural network | on-site measurement | exogenous information

Energies , 2013

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Hourly Water Level Forecasting at Tributary Affected by Main River Condition

Ji Youn Sung, Jeongwoo Lee, Il-Moon Chung, Jun-Haeng Heo

This study develops hourly water level forecasting models with lead-times of 1 to 3 h using an artificial neural network (ANN) for Anyangcheon stream, one of the major tributaries of the Han River, South Korea. To consider the backwater effect from this r... see more

tributary | hourly water level forecasting | artificial neural network

Water , 2017

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A Multiple Access Protocol Based on Gray Forecast for Satellite Network

Chong Han, Zilong Li, Jian Zhou, Lijuan Sun and Siyu Chen

A satellite network’s multiple access protocol must be well designed to efficiently occupy channel resources and meet various qualities of service requirements. In this paper, a combined free/demand assignment multiple access protocol based on gray ... see more

satellite network | gray forecast | multiple access | bandwidth allocation

Future Internet , 2018

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Forecasting the Number of Students in Multiple Linear Regressions

Fristi Riandari, Hengki Tamando Sihotang, Husain Husain

The most important element of higher education was students, therefore every university must continue to improve services in the future, and one of them was by using decision support. This case could be done by utilizing the University of Big Data. Predic... see more

Big data | Data Mining | Multiple linear regressions | Forecasting

MATRIK : Jurnal Manajemen; Teknik Informatika dan Rekayasa Komputer , 2028

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The Electrical Load Forecasting Base on an Optimal Selection Method of Multiple Models in DSM

Guilin Zheng,Li Zhang

Electrical load forecasting plays a key role in energy scheduling and planning. It is a challenge to predict electric load accurately due to the versatility of electrical loads and the vast number of users in DSM of low-voltage side. Most of electrical lo... see more

Optimal selection method | Load forecasting | Demand Side Management (DSM) | Low-voltage side

International Journal of Online Engineering (iJOE) , 2015

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Forecasting based on Bayesian type models

Peter Bidyuk, Aleksander Peter Gozhjy, Alexandr T rofymchuk

A review of some Bayesian data analysis models is proposed, namely the models with one and several parameters. A methodology is developed for probabilistic models construction in the form of Bayesian networks using statistical data and expert estimates. T... see more

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY , 2016

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The use of radial basis function and non-linear autoregressive exogenous neural networks to forecast multi-step ahead of time flood water level

(1) Amrul Faruq (University of Muhammadiyah Malang, Indonesia) (2) Shahrum Shah Abdullah (Department of Electronics System and Electrical Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Malaysia) (3) Aminaton Marto (Department of Environmental Engineering and Green Technology, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Malaysia) (4) Mohd Anuar Abu Bakar (Department of Electronics System and Electrical Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Malaysia) (5) Shamsul Faisal Mohd Hussein (Department of Electronics System and Electrical Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Malaysia) (6) Che Munira Che Razali (Department of Electronics System and Electrical Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Malaysia)

Many different Artificial Neural Networks (ANN) models of flood have been developed for forecast updating. However, the model performance, and error prediction in which forecast outputs are adjusted directly based on models calibrated to the time series o... see more

Floods | Forecasting | Radial basis function | NARX | Artificial neural networks

International Journal of Advances in Intelligent Informatics , 2019

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