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Applied Econometrics

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ISBN: 9783038979265 9783038979272 Year: Pages: 222 DOI: 10.3390/books978-3-03897-927-2 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Science (General) --- Mathematics
Added to DOAB on : 2019-06-26 08:44:06
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Abstract

Although the theme of the monograph is primarily related to “Applied Econometrics”, there are several theoretical contributions that are associated with empirical examples, or directions in which the novel theoretical ideas might be applied. The monograph is associated with significant and novel contributions in theoretical and applied econometrics; economics; theoretical and applied financial econometrics; quantitative finance; risk; financial modeling; portfolio management; optimal hedging strategies; theoretical and applied statistics; applied time series analysis; forecasting; applied mathematics; energy economics; energy finance; tourism research; tourism finance; agricultural economics; informatics; data mining; bibliometrics; and international rankings of journals and academics.

Keywords

inflation --- postage stamps --- price recovery --- historical time series --- EGARCH --- FHA loan --- home mortgage --- foreclosure --- default and prepayment --- unobserved heterogeneity --- duration models --- competing risks --- earnings forecasts --- earnings announcements --- financial markets --- financial analysts --- nonparametric time series modeling --- nonlinearity --- unified time series algorithm --- exploratory diagnostics --- control environment --- budgetary system and strategies --- operational control --- company performance --- economic growth --- economic freedom --- foreign direct investment --- panel data --- cash payments --- efficiency --- denomination range --- JEL Classification --- E42 --- E58 --- managing of financial health --- risk of bankruptcy --- prediction methods --- post-communist countries --- Misery Index --- inflation --- unemployment --- Probit and Logit models --- Okun’s law --- multivariate regression models --- heavy-tailed data --- Mahalanobis distances --- maximum likelihood estimator --- independent multivariate Student distribution --- uncorrelated multivariate Student distribution --- derivatives market --- economic development --- Granger-causality tests --- vector error correction model (VECM) --- DOLS --- FMOLS --- income inequality --- economic growth --- middle income countries --- Granger causality test --- system GMM --- oil price --- exchange rate --- trade balance --- cointegration --- frequency domain causality --- Nigeria --- Fama-French factor model --- market microstructure --- trading behavior --- panel data factor model --- social network model --- risk spillover --- abnormal returns

Intelligent Optimization Modelling in Energy Forecasting

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ISBN: 9783039283644 9783039283651 Year: Pages: 262 DOI: 10.3390/books978-3-03928-365-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2020-04-07 23:07:09
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Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, etc.) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, etc.). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, etc.) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, etc.), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting.

Keywords

short-term load forecasting --- weighted k-nearest neighbor (W-K-NN) algorithm --- comparative analysis --- empirical mode decomposition (EMD) --- particle swarm optimization (PSO) algorithm --- intrinsic mode function (IMF) --- support vector regression (SVR) --- short term load forecasting --- crude oil price forecasting --- time series forecasting --- hybrid model --- complementary ensemble empirical mode decomposition (CEEMD) --- sparse Bayesian learning (SBL) --- multi-step wind speed prediction --- Ensemble Empirical Mode Decomposition --- Long Short Term Memory --- General Regression Neural Network --- Brain Storm Optimization --- substation project cost forecasting model --- feature selection --- data inconsistency rate --- modified fruit fly optimization algorithm --- deep convolutional neural network --- multi-objective grey wolf optimizer --- long short-term memory --- fuzzy time series --- LEM2 --- combination forecasting --- wind speed --- electrical power load --- crude oil prices --- time series forecasting --- improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) --- kernel learning --- kernel ridge regression --- differential evolution (DE) --- artificial intelligence techniques --- energy forecasting --- condition-based maintenance --- asset management --- renewable energy consumption --- Gaussian processes regression --- state transition algorithm --- five-year project --- forecasting --- Markov-switching --- Markov-switching GARCH --- energy futures --- commodities --- portfolio management --- active investment --- diversification --- institutional investors --- energy price hedging --- metamodel --- ensemble --- individual --- regression --- interpolation

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