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Computational Intelligence Techniques with Application to Crude Oil Price Projection: A Literature Survey from 2001- 2012

H. Chiroma, S. Abdulkareem, A. Abubakar, M. Joda Usman
2013 Neural Network World  
This paper is an attempt to survey the applications of computational intelligence techniques for predicting crude oil prices over a period of ten years.  ...  The reviewed literature covers a spectrum of publications on the proposed model, source of experimental data, period of data collection, year of publication and contributors.  ...  [87] used a three step approach to predict international crude oil prices.  ... 
doi:10.14311/nnw.2013.23.032 fatcat:35opoiffefggpgbyxbj4cxhsnu

Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price

Kaijian He, Rui Zha, Jun Wu, Kin Lai
2016 Sustainability  
In this paper we propose a new multivariate Empirical Mode Decomposition (EMD)-based model to take advantage of these heterogeneous characteristics of the price movement and model them in the crude oil  ...  Empirical studies in benchmark crude oil markets confirm that more diverse heterogeneous data characteristics can be revealed and modeled in the projected time delayed domain.  ...  The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  ... 
doi:10.3390/su8040387 fatcat:ctyp4b42wfcqrjskcc4iphazey

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Krzysztof Drachal, Michał Pawłowski
2021 Economies  
In particular, it combines three important—yet not often jointly discussed—topics: genetic algorithms, their hybrids with other tools, and commodity price forecasting issues.  ...  Indeed, the latter case is very frequent while forecasting the commodity prices of, for example, crude oil. Moreover, growing interest in their application has been observed recently.  ...  In particular, the highest interest is given to forecasting the price of crude oil and some other energy commodities; metal prices, with a closer look at the prices of copper and gold; and certain agricultural  ... 
doi:10.3390/economies9010006 fatcat:ibz4aueq45aolay2u6nzw3fjyq

Renewable Power Output Forecasting Using Least-Squares Support Vector Regression and Google Data

Kuen-Suan Chen, Kuo-Ping Lin, Jun-Xiang Yan, Wan-Lin Hsieh
2019 Sustainability  
This study attempts to develop a novel least-squares support vector regression with a Google (LSSVR-G) model to accurately forecast power output with renewable power, thermal power, and nuclear power outputs  ...  This study integrates a Google application programming interface (API), least-squares support vector regression (LSSVR), and a genetic algorithm (GA) to develop a novel LSSVR-G model for accurately forecasting  ...  Acknowledgments: The authors would like to express appreciation to the Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Contract Numbers  ... 
doi:10.3390/su11113009 fatcat:z6evb6zo25e4jbtzdrtsmjwl3u

Forecasting methods in energy planning models

Kumar Biswajit Debnath, Monjur Mourshed
2018 Renewable & Sustainable Energy Reviews  
The objective of this review is, therefore, to analyze the methods utilized in different EPMs to investigate their accuracy, objective, temporal and spatial extents with a view to identify the factors  ...  The identified main topics were: energy demand and/or supply model and/or forecasting; energy planning models; emission reduction models; time series analysis; and forecasting.  ...  ., A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China. Energy, 2011. 36(11):6542-54. 145.  ... 
doi:10.1016/j.rser.2018.02.002 fatcat:ypu23l2uobcrdhylxv324wgnq4

An empirical study on the various stock market prediction methods

Jaymit Bharatbhai Pandya, Udesang K. Jaliya
2022 Register: Jurnal Ilmiah Teknologi Sistem Informasi  
A proper set of such representative factors must be analyzed to make an efficient prediction model. Marginal improvement of prediction accuracy can be gainful for investors.  ...  The techniques are further investigated to analyze relations of prediction methods with feature selection algorithm, datasets, feature selection methods, and input parameters.  ...  Jaliya: Conceptulization, investigation, resouces, software, supervision, validation, visualization and wrtingreview & editing.  ... 
doi:10.26594/register.v8i1.2533 fatcat:vtircrcxzzg3hinmpwszwrlkhu

Multi-period portfolio selection with drawdown control

Peter Nystrup, Stephen Boyd, Erik Lindström, Henrik Madsen
2018 Annals of Operations Research  
In this paper, we propose a novel hierarchical model to forecast China's foreign trade.  ...  Existing works focus on point forecasts through the lens of linear models. We apply nonlinear models to investigate the impact of oil price on higher moments of the growth.  ...  We study various business areas with both short and long product life cycles and present numerical results to prove effectiveness of the proposed methods.  ... 
doi:10.1007/s10479-018-2947-3 fatcat:haworusnrfcqvlmkmbidfwb5wu

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 [article]

Omer Berat Sezer, Mehmet Ugur Gudelek, Ahmet Murat Ozbayoglu
2019 arXiv   pre-print
Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly.  ...  Financial time series forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas  ...  In [158] , authors tried to predict WTI crude oil prices using several models including combinations of DBN, LSTM, Autoregressive Moving Average (ARMA) and RW.  ... 
arXiv:1911.13288v1 fatcat:npvyhewuvvcvri4e43jwj3c45y

A Holistic Auto-Configurable Ensemble Machine Learning Strategy for Financial Trading

Salvatore Carta, Andrea Corriga, Anselmo Ferreira, Diego Reforgiato Recupero, Roberto Saia
2019 Computation  
To tackle these issues, this paper introduces a fully automated optimized ensemble approach, where an optimized feature selection process has been combined with an automatic ensemble machine learning strategy  ...  A series of experiments performed on different real-world futures markets demonstrate the effectiveness of such an approach with regard to both to the Buy and Hold baseline strategy and to several canonical  ...  The authors of [54] combined results of bivariate empirical mode decomposition, interval Multilayer Perceptrons, and an interval exponential smoothing method to predict crude oil prices.  ... 
doi:10.3390/computation7040067 fatcat:bcyqnar6vvcnpeqbfpb2daxrl4

Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review

Manel Rhif, Ali Ben Abbes, Imed Farah, Beatriz Martínez, Yanfang Sang
2019 Applied Sciences  
In fact, several decomposition methods were developed in order to extract various components (e.g., seasonal, trend and abrupt components) from the non-stationary TS, which allows for an improved interpretation  ...  Finally, five challenges and future works, such as the selection of the type of wavelet, selection of the adequate mother wavelet, selection of the scale, the combination between wavelet transform and  ...  of oil prices and constructing the multi-period network model of the global oil price co-movement.  ... 
doi:10.3390/app9071345 fatcat:lckeoydwzva3znqq2d753pgo6q

Application of Long Short-Term Memory (LSTM) Neural Network Based on Deep Learning for Electricity Energy Consumption Forecasting

2021 Turkish Journal of Electrical Engineering and Computer Sciences  
As a consequence, the LSTM model generally outperformed all ANFIS models. 16 The results revealed that forecasting of short-term daily EEC time series using the LSTM approach can provide high 17 accuracy  ...  Those forecasted results by 11 the LSTM, ANFIS-FCM, ANFIS-SC and ANFIS-GP models were evaluated by comparing with the actual data using 12 statistical accuracy metrics.  ...  Forecasting electricity load by a novel recurrent extreme learning machines approach. Int. J. Electr.  ... 
doi:10.3906/elk-2011-14 fatcat:dhpvdtuzjngfrfcnecp5jeu4n4

A Novel Carbon Price Fluctuation Trend Prediction Method Based on Complex Network and Classification Algorithm

Hua Xu, Minggang Wang, Guilherme Ferraz de Arruda
2021 Complexity  
In order to promote the accuracy of the forecasting model, this paper proposes the idea of integrating network topology information into carbon price data; that is, carbon price data are mapped into a  ...  Carbon price fluctuation is affected by both internal market mechanisms and the heterogeneous environment. Moreover, it is a complex dynamic evolution process.  ...  , the multiscale nonlinear ensemble leaning paradigm [24] , the variational mode decomposition and optimal combined model [25] , the model based on secondary decomposition algorithm and optimized back  ... 
doi:10.1155/2021/3052041 fatcat:zxuv6be6u5h57dekvwfjrutbua

Forecasting Models of Electricity Prices

Javier Contreras
2017 Energies  
(swi.smartwatt.net) for providing data and practical experience associated with the models of this paper.  ...  Author Contributions: All authors conceived of and designed the forecasting study. Bartosz Uniejewski performed the numerical experiments. Jakub Nowotarski and Rafał Weron supervised the experiments.  ...  Previously, ensemble models for price prediction have been proposed in different fields, e.g., crude oil price [16] and carbon price [17] .  ... 
doi:10.3390/en10020160 fatcat:tfm7gy4vkfhqxj4pdts7vt5wka

Is It Possible to Forecast the Price of Bitcoin?

Julien Chevallier, Dominique Guégan, Stéphane Goutte
2021 Forecasting  
This paper focuses on forecasting the price of Bitcoin, motivated by its market growth and the recent interest of market participants and academics.  ...  The main contribution is to use these data analytics techniques with great caution in the parameterization, instead of classical parametric modelings (AR), to disentangle the non-stationary behavior of  ...  In these ensemble methods, several weaker decision trees are combined into a more robust ensemble.  ... 
doi:10.3390/forecast3020024 fatcat:5nocd7gm3zhrpfbgi3tgejjqdm

Neural forecasting: Introduction and literature overview [article]

Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Bernie Wang, Danielle Maddix, Caner Turkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, Laurent Callot, Tim Januschowski
2020 arXiv   pre-print
As the prevalence of neural network based solutions among the best entries in the recent M4 competition shows, the recent popularity of neural forecasting methods is not limited to industry and has also  ...  Building on these foundations, the article then gives an overview of the recent literature on neural networks for forecasting and applications.  ...  [138] use a nonlinear autoregressive model (NARX), an alternative to LSTMs. For crude oil price forecasting, [124] use a CNN in a model pipeline to predict crude oil prices.  ... 
arXiv:2004.10240v1 fatcat:i3nbsw5sojckdee5d4magpwsl4
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