Oil Tanker Markets Modeling, Analysis and Forecasting Using Neural Networks, Fuzzy Logic and Genetic Algorithms PDF Download

Author: Chun Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 474

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Author: Mitsuo Gen
Publisher: John Wiley & Sons
ISBN: 9780471315315
Category : Technology & Engineering
Languages : en
Pages : 520

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Book Description
Im Mittelpunkt dieses Buches steht eines der wichtigsten Optimierungsverfahren der industriellen Ingenieurtechnik: Mit Hilfe genetischer Algorithmen lassen sich Qualität, Design und Zuverlässigkeit von Produkten entscheidend verbessern. Das Verfahren beruht auf der Wahrscheinlichkeitstheorie und lehnt sich an die Prinzipien der biologischen Vererbung an: Die Eigenschaften des Produkts werden, unter Beachtung der äußeren Randbedingungen, schrittweise optimiert. Ein hochaktueller Band international anerkannter Autoren. (03/00)

Author: G David Garson
Publisher: SAGE
ISBN: 0857026275
Category : Social Science
Languages : en
Pages : 201

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This book provides the first accessible introduction to neural network analysis as a methodological strategy for social scientists. The author details numerous studies and examples which illustrate the advantages of neural network analysis over other quantitative and modelling methods in widespread use. Methods are presented in an accessible style for readers who do not have a background in computer science. The book provides a history of neural network methods, a substantial review of the literature, detailed applications, coverage of the most common alternative models and examples of two leading software packages for neural network analysis.

Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 824

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Author: Ly, Racine
Publisher: Intl Food Policy Res Inst
ISBN:
Category : Political Science
Languages : en
Pages : 26

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This paper applies a recurrent neural network (RNN) method to forecast cotton and oil prices. We show how these new tools from machine learning, particularly Long-Short Term Memory (LSTM) models, complement traditional methods. Our results show that machine learning methods fit reasonably well with the data but do not outperform systematically classical methods such as Autoregressive Integrated Moving Average (ARIMA) or the naïve models in terms of out of sample forecasts. However, averaging the forecasts from the two type of models provide better results compared to either method. Compared to the ARIMA and the LSTM, the Root Mean Squared Error (RMSE) of the average forecast was 0.21 and 21.49 percent lower, respectively, for cotton. For oil, the forecast averaging does not provide improvements in terms of RMSE. We suggest using a forecast averaging method and extending our analysis to a wide range of commodity prices.

Author: Marco A. C. Pacheco
Publisher: Springer
ISBN: 3540930000
Category : Technology & Engineering
Languages : en
Pages : 296

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Book Description
The decision to invest in oil field development is an extremely complex problem, even in the absence of uncertainty, due to the great number of technological alternatives that may be used, to the dynamic complexity of oil reservoirs - which involves mul- phase flows (oil, gas and water) in porous media with phase change, and to the c- plicated combinatorial optimization problem of choosing the optimal oil well network, that is, choosing the number and types of wells (horizontal, vertical, directional, m- tilateral) required for draining oil from a field with a view to maximizing its economic value. This problem becomes even more difficult when technical uncertainty and e- nomic uncertainty are considered. The former are uncertainties regarding the existence, volume and quality of a reservoir and may encourage an investment in information before the field is developed, in order to reduce these uncertainties and thus optimize the heavy investments required for developing the reservoir. The economic or market uncertainties are associated with the general movements of the economy, such as oil prices, gas demand, exchange rates, etc. , and may lead decision-makers to defer - vestments and wait for better market conditions. Choosing the optimal investment moment under uncertainty is a complex problem which traditionally involves dynamic programming tools and other techniques that are used by the real options theory.

Author: Saud Al-Fattah
Publisher:
ISBN:
Category :
Languages : en
Pages : 6

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This paper develops a rigorous and advanced computational model to describe, analyze, and forecast global crude oil demand. The paper deploys a hybrid approach of artificial intelligence techniques: artificial neural network and genetic algorithms, to devise a methodological framework for developing forecasting models of global crude oil demand. We piloted two country cases of a high oil producer (Saudi Arabia) and a high oil consumer (China) to illustrate the effectiveness and applicability of the proposed methodology for developing oil demand outlook using artificial intelligence.The input variables of the neural network models include gross domestic product, population, oil prices, gas prices, and transport data, in addition to transformed variables and functional links. The artificial intelligent predictive models of oil demand were successfully developed, trained, validated and tested using historical oil-market data yielding excellent predictions of oil demand. The performance of the intelligent models of Saudi Arabia and China were examined for generalization attribute, predictability, and accuracy. Oil demand models for Saudi Arabia and China achieved a high prediction accuracy of a correlation coefficient of 0.975 and 0.996, respectively.The intelligent outlook models show that crude oil demand for both Saudi Arabia and China will continue to increase for the outlook period (2018-2022) but with mildly declining growth. This falling growth of oil demand can be attributed to the increase in energy efficiency, fuel switching, conversion of power plants from crude to gas-based plants, and an increase in the utilization of renewable energy such as solar and wind for electric generation and water desalination.The methodology proposed improves and enhances the conventional process of developing the oil demand outlook. It also improves and enhances the predictability and accuracy of current forecasting models of oil demand. In this study, features selection techniques are applied to identify and understand the endogenous and exogenous factors that influence global energy markets, particularly those factors that impact and drive global oil demand.

Author: Joish Bosco
Publisher: GRIN Verlag
ISBN: 3668800456
Category : Computers
Languages : en
Pages : 82

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Book Description
Project Report from the year 2018 in the subject Computer Science - Technical Computer Science, , course: Computer Science, language: English, abstract: Modeling and Forecasting of the financial market have been an attractive topic to scholars and researchers from various academic fields. The financial market is an abstract concept where financial commodities such as stocks, bonds, and precious metals transactions happen between buyers and sellers. In the present scenario of the financial market world, especially in the stock market, forecasting the trend or the price of stocks using machine learning techniques and artificial neural networks are the most attractive issue to be investigated. As Giles explained, financial forecasting is an instance of signal processing problem which is difficult because of high noise, small sample size, non-stationary, and non-linearity. The noisy characteristics mean the incomplete information gap between past stock trading price and volume with a future price. The stock market is sensitive with the political and macroeconomic environment. However, these two kinds of information are too complex and unstable to gather. The above information that cannot be included in features are considered as noise. The sample size of financial data is determined by real-world transaction records. On one hand, a larger sample size refers a longer period of transaction records; on the other hand, large sample size increases the uncertainty of financial environment during the 2 sample period. In this project, we use stock data instead of daily data in order to reduce the probability of uncertain noise, and relatively increase the sample size within a certain period of time. By non-stationarity, one means that the distribution of stock data is various during time changing. Non-linearity implies that feature correlation of different individual stocks is various. Efficient Market Hypothesis was developed by Burton G. Malkiel in 1991.

Author:
Publisher:
ISBN:
Category : Dissertation abstracts
Languages : en
Pages : 872

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Author: Parniyan Mousaie
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Book Description
It is vitally crucial to establish a method that can accurately forecast prices on the stock exchange market because of the influence the stock market has on the country's ability to raise capital and advance its economic growth. On the stock market, a great number of sensitivity factors are connected to price movement, which is why the progressions associated with such a phenomenon are routinely evaluated. Several neural network models have recently been used to forecast stock prices. In this research, the data related to active companies in the stock market was used to evaluate research questions. Also, the neural network technique was used to look at all data from the market index, fuzzy neural network model, and long short-term memory (LSTM) model from 2020 to 2021. Accordingly, this study aims to forecast the stock price and give a dynamic model with fewer errors using integrated factors, the technical, cardinal, and economic assessment of the market index using the neural network technique. This will be accomplished by utilizing the neural network method. The findings demonstrated that if the combined data of basic analytical factors was used further, we would not only have better training and receive better results, but we would also be able to decrease the prediction error.