Sesame Price Prediction Using Artificial Neural Network

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Sesame Price Prediction Using Artificial Neural Network

Author : Endalamaw Gashaw
Publisher : GRIN Verlag
ISBN 13 : 3346135187
Total Pages : 69 pages
Book Rating : 4.3/5 (461 download)

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Book Synopsis Sesame Price Prediction Using Artificial Neural Network by : Endalamaw Gashaw

Download or read book Sesame Price Prediction Using Artificial Neural Network written by Endalamaw Gashaw and published by GRIN Verlag. This book was released on 2020-03-23 with total page 69 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2019 in the subject Computer Science - Miscellaneous, University of Gondar (Atse Tewodros Cumpas), course: Information technology, language: English, abstract: Agricultural price predictions are an integral component of trade and policy analysis. As the prices of agricultural commodities directly influence the real income of farmers and it also affects the national foreign currency generate. Sesame is highly produced in some tropical and subtropical rain forest Ethiopia region. The thesis is to build a model that can predict market prices of sesame commodity. Based on the complexity of sesame price prediction; the predicting models used for crop are linear regression, support vector machine and neural network models to predict a future price. A data have been taken from the ECX website (www.ecx.com.et) in the interval of January 2013 to March 2019. The total numbers of records selected to the experiments are 5,327 daily prices are used for proposed models. The experimental result had evaluated by RMSE, MSE and CC metrics. We follow six phase CRISP-DM process model for sesame price prediction. The process phase are, business understanding, data understanding, data preparation, modeling, evaluating and deployment.

Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing

Author : Valentina Emilia Balas
Publisher : Springer Nature
ISBN 13 : 9813349689
Total Pages : 795 pages
Book Rating : 4.8/5 (133 download)

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Book Synopsis Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing by : Valentina Emilia Balas

Download or read book Proceedings of International Conference on Computational Intelligence, Data Science and Cloud Computing written by Valentina Emilia Balas and published by Springer Nature. This book was released on 2021 with total page 795 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes selected papers presented at International Conference on Computational Intelligence, Data Science and Cloud Computing (IEM-ICDC) 2020, organized by the Department of Information Technology, Institute of Engineering & Management, Kolkata, India, during 25-27 September 2020. It presents substantial new research findings about AI and robotics, image processing and NLP, cloud computing and big data analytics as well as in cyber security, blockchain and IoT, and various allied fields. The book serves as a reference resource for researchers and practitioners in academia and industry.

Using Artificial Neural Networks for Timeseries Smoothing and Forecasting

Author : Jaromír Vrbka
Publisher : Springer Nature
ISBN 13 : 3030756491
Total Pages : 197 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Using Artificial Neural Networks for Timeseries Smoothing and Forecasting by : Jaromír Vrbka

Download or read book Using Artificial Neural Networks for Timeseries Smoothing and Forecasting written by Jaromír Vrbka and published by Springer Nature. This book was released on 2021-09-04 with total page 197 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this publication is to identify and apply suitable methods for analysing and predicting the time series of gold prices, together with acquainting the reader with the history and characteristics of the methods and with the time series issues in general. Both statistical and econometric methods, and especially artificial intelligence methods, are used in the case studies. The publication presents both traditional and innovative methods on the theoretical level, always accompanied by a case study, i.e. their specific use in practice. Furthermore, a comprehensive comparative analysis of the individual methods is provided. The book is intended for readers from the ranks of academic staff, students of universities of economics, but also the scientists and practitioners dealing with the time series prediction. From the point of view of practical application, it could provide useful information for speculators and traders on financial markets, especially the commodity markets.

Forecasting commodity prices using long-short-term memory neural networks

Author : Ly, Racine
Publisher : Intl Food Policy Res Inst
ISBN 13 :
Total Pages : 26 pages
Book Rating : 4./5 ( download)

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Book Synopsis Forecasting commodity prices using long-short-term memory neural networks by : Ly, Racine

Download or read book Forecasting commodity prices using long-short-term memory neural networks written by Ly, Racine and published by Intl Food Policy Res Inst. This book was released on 2021-02-10 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network

Author : Joish Bosco
Publisher : GRIN Verlag
ISBN 13 : 3668800456
Total Pages : 76 pages
Book Rating : 4.6/5 (688 download)

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Book Synopsis Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network by : Joish Bosco

Download or read book Stock Market Prediction and Efficiency Analysis using Recurrent Neural Network written by Joish Bosco and published by GRIN Verlag. This book was released on 2018-09-18 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Foreign-Exchange-Rate Forecasting with Artificial Neural Networks

Author : Lean Yu
Publisher : Springer Science & Business Media
ISBN 13 : 038771720X
Total Pages : 323 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Foreign-Exchange-Rate Forecasting with Artificial Neural Networks by : Lean Yu

Download or read book Foreign-Exchange-Rate Forecasting with Artificial Neural Networks written by Lean Yu and published by Springer Science & Business Media. This book was released on 2010-02-26 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on forecasting foreign exchange rates via artificial neural networks (ANNs), creating and applying the highly useful computational techniques of Artificial Neural Networks (ANNs) to foreign-exchange rate forecasting. The result is an up-to-date review of the most recent research developments in forecasting foreign exchange rates coupled with a highly useful methodological approach to predicting rate changes in foreign currency exchanges.

Artificial Neural Networks

Author : Ali Roghani
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781536976830
Total Pages : 108 pages
Book Rating : 4.9/5 (768 download)

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Book Synopsis Artificial Neural Networks by : Ali Roghani

Download or read book Artificial Neural Networks written by Ali Roghani and published by Createspace Independent Publishing Platform. This book was released on 2016-08-09 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are state-of-the-art, trainable algorithms that emulate certain major aspects in the functioning of the human brain. This gives them a unique, self-training ability, the ability to formalize unclassified information and, most importantly, the ability to make forecasts based on the historical information they have at their disposal. Neural networks have been used increasingly in a variety of business applications, including forecasting and marketing research solutions. In some areas, such as fraud detection or risk assessment, they are the indisputable leaders. The major fields in which neural networks have found application are financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks can be applied gainfully by all kinds of traders, so if you're a trader and you haven't yet been introduced to neural networks, we'll take you through this method of technical analysis and show you how to apply it to your trading style. Neural networks have been touted as all-powerful tools in stock-market prediction. Companies such as MJ Futures claim amazing 199.2% returns over a 2-year period using their neural network prediction methods. They also claim great ease of use; as technical editor John Sweeney said in a 1995 issue of "Technical Analysis of Stocks and Commodities," "you can skip developing complex rules (and redeveloping them as their effectiveness fades) . . . just define the price series and indicators you want to use, and the neural network does the rest."

Artificial Neural Networks

Author : Ali Roghani
Publisher : CreateSpace
ISBN 13 : 9781511712330
Total Pages : 108 pages
Book Rating : 4.7/5 (123 download)

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Book Synopsis Artificial Neural Networks by : Ali Roghani

Download or read book Artificial Neural Networks written by Ali Roghani and published by CreateSpace. This book was released on 2015-04-17 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are state-of-the-art, trainable algorithms that emulate certain major aspects in the functioning of the human brain. This gives them a unique, self-training ability, the ability to formalize unclassified information and, most importantly, the ability to make forecasts based on the historical information they have at their disposal. Neural networks have been used increasingly in a variety of business applications, including forecasting and marketing research solutions. In some areas, such as fraud detection or risk assessment, they are the indisputable leaders. The major fields in which neural networks have found application are financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks can be applied gainfully by all kinds of traders, so if you're a trader and you haven't yet been introduced to neural networks, we'll take you through this method of technical analysis and show you how to apply it to your trading style. Neural networks have been touted as all-powerful tools in stock-market prediction. Companies such as MJ Futures claim amazing 199.2% returns over a 2-year period using their neural network prediction methods. They also claim great ease of use; as technical editor John Sweeney said in a 1995 issue of "Technical Analysis of Stocks and Commodities," "you can skip developing complex rules (and redeveloping them as their effectiveness fades) . . . just define the price series and indicators you want to use, and the neural network does the rest."

Financial Prediction Using Neural Networks

Author : Joseph S. Zirilli
Publisher :
ISBN 13 :
Total Pages : 168 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Financial Prediction Using Neural Networks by : Joseph S. Zirilli

Download or read book Financial Prediction Using Neural Networks written by Joseph S. Zirilli and published by . This book was released on 1997 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focusing on approaches to performing trend analysis through the use of neural nets, this book comparess the results of experiments on various types of markets, and includes a review of current work in the area. It appeals to students in both neural computing and finance as well as to financial analysts and academic and professional researchers in the field of neural network applications.

Artificial Higher Order Neural Networks for Economics and Business

Author : Zhang, Ming
Publisher : IGI Global
ISBN 13 : 1599048981
Total Pages : 542 pages
Book Rating : 4.5/5 (99 download)

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Book Synopsis Artificial Higher Order Neural Networks for Economics and Business by : Zhang, Ming

Download or read book Artificial Higher Order Neural Networks for Economics and Business written by Zhang, Ming and published by IGI Global. This book was released on 2008-07-31 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is the first book to provide opportunities for millions working in economics, accounting, finance and other business areas education on HONNs, the ease of their usage, and directions on how to obtain more accurate application results. It provides significant, informative advancements in the subject and introduces the HONN group models and adaptive HONNs"--Provided by publisher.

Artificial Neural Networks in Finance and Manufacturing

Author : Kamruzzaman, Joarder
Publisher : IGI Global
ISBN 13 : 1591406722
Total Pages : 299 pages
Book Rating : 4.5/5 (914 download)

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Book Synopsis Artificial Neural Networks in Finance and Manufacturing by : Kamruzzaman, Joarder

Download or read book Artificial Neural Networks in Finance and Manufacturing written by Kamruzzaman, Joarder and published by IGI Global. This book was released on 2006-03-31 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book presents a variety of practical applications of neural networks in two important domains of economic activity: finance and manufacturing"--Provided by publisher.

A Performance Analysis of Machine Learning Techniques in Stock Price Prediction

Author : Hasan Al-Quaid
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (142 download)

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Book Synopsis A Performance Analysis of Machine Learning Techniques in Stock Price Prediction by : Hasan Al-Quaid

Download or read book A Performance Analysis of Machine Learning Techniques in Stock Price Prediction written by Hasan Al-Quaid and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stock market trends are of great interest to investors and corporations worldwide. The global financial system is intricately interconnected with the stock market, playing a central role in driving economic activity. In today's interconnected world, trading stocks has become a popular and accessible means for individuals and entities to generate income. Numerous academic researchers have explored the use of Artificial Intelligence (AI) for stock prediction and have claimed that their models can accurately forecast stock performance. The issue is that many of these studies rely on a single data source, namely, daily stock data and cannot predict future stock prices, more than 1 or 2 days, with a large degree of success. Additionally, the single data source may be influenced by a multitude of economic factors as well as public sentiment, which is the most significant. In this research paper, several of these AI models are tested to evaluate their claims regarding stock prediction capabilities. Based on our experiments utilizing AI models and the results gathered, it was concluded that it was not possible to predict future stock prices using one method alone. Therefore, in order to provide a greater accuracy in predicting future stocks, the use of an ensemble approach was proposed. While many researchers build their ensemble models by combining various Artificial Neural Network models with sentiment analysis. We have suggested a different approach using other kinds of AI models, along with enhancements to traditional sentiment analysis techniques.

Neural Networks and the Financial Markets

Author : Jimmy Shadbolt
Publisher : Springer Science & Business Media
ISBN 13 : 1447101510
Total Pages : 266 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Neural Networks and the Financial Markets by : Jimmy Shadbolt

Download or read book Neural Networks and the Financial Markets written by Jimmy Shadbolt and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume looks at financial prediction from a broad range of perspectives. It covers: - the economic arguments - the practicalities of the markets - how predictions are used - how predictions are made - how predictions are turned into something usable (asset locations) It combines a discussion of standard theory with state-of-the-art material on a wide range of information processing techniques as applied to cutting-edge financial problems. All the techniques are demonstrated with real examples using actual market data, and show that it is possible to extract information from very noisy, sparse data sets. Aimed primarily at researchers in financial prediction, time series analysis and information processing, this book will also be of interest to quantitative fund managers and other professionals involved in financial prediction.

The Application of Neural Networks in the Forecasting of Share Prices

Author : Robert J. Van Eyden
Publisher : Finance and Technology Publishing
ISBN 13 : 9780965133203
Total Pages : 285 pages
Book Rating : 4.1/5 (332 download)

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Book Synopsis The Application of Neural Networks in the Forecasting of Share Prices by : Robert J. Van Eyden

Download or read book The Application of Neural Networks in the Forecasting of Share Prices written by Robert J. Van Eyden and published by Finance and Technology Publishing. This book was released on 1996-01-01 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Stock price Prediction a referential approach on how to predict the stock price using simple time series...

Author : Dr.N.Srinivasan
Publisher : Clever Fox Publishing
ISBN 13 :
Total Pages : 56 pages
Book Rating : 4./5 ( download)

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Book Synopsis Stock price Prediction a referential approach on how to predict the stock price using simple time series... by : Dr.N.Srinivasan

Download or read book Stock price Prediction a referential approach on how to predict the stock price using simple time series... written by Dr.N.Srinivasan and published by Clever Fox Publishing. This book was released on with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about the various techniques involved in the stock price prediction. Even the people who are new to this book, after completion they can do stock trading individually with more profit.

Prediction of Stock Market Index Movements with Machine Learning

Author : Nazif AYYILDIZ
Publisher : Özgür Publications
ISBN 13 : 975447821X
Total Pages : 121 pages
Book Rating : 4.7/5 (544 download)

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Book Synopsis Prediction of Stock Market Index Movements with Machine Learning by : Nazif AYYILDIZ

Download or read book Prediction of Stock Market Index Movements with Machine Learning written by Nazif AYYILDIZ and published by Özgür Publications. This book was released on 2023-12-16 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book titled "Prediction of Stock Market Index Movements with Machine Learning" focuses on the performance of machine learning methods in forecasting the future movements of stock market indexes and identifying the most advantageous methods that can be used across different stock exchanges. In this context, applications have been conducted on both developed and emerging market stock exchanges. The stock market indexes of developed countries such as NYSE 100, NIKKEI 225, FTSE 100, CAC 40, DAX 30, FTSE MIB, TSX; and the stock market indexes of emerging countries such as SSE, BOVESPA, RTS, NIFTY 50, IDX, IPC, and BIST 100 were selected. The movement directions of these stock market indexes were predicted using decision trees, random forests, k-nearest neighbors, naive Bayes, logistic regression, support vector machines, and artificial neural networks methods. Daily dataset from 01.01.2012 to 31.12.2021, along with technical indicators, were used as input data for analysis. According to the results obtained, it was determined that artificial neural networks were the most effective method during the examined period. Alongside artificial neural networks, logistic regression and support vector machines methods were found to predict the movement direction of all indexes with an accuracy of over 70%. Additionally, it was noted that while artificial neural networks were identified as the best method, they did not necessarily achieve the highest accuracy for all indexes. In this context, it was established that the performance of the examined methods varied among countries and indexes but did not differ based on the development levels of the countries. As a conclusion, artificial neural networks, logistic regression, and support vector machines methods are recommended as the most advantageous approaches for predicting stock market index movements.

Intelligent Systems and Financial Forecasting

Author : Jason Kingdon
Publisher : Springer
ISBN 13 :
Total Pages : 248 pages
Book Rating : 4.:/5 (321 download)

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Book Synopsis Intelligent Systems and Financial Forecasting by : Jason Kingdon

Download or read book Intelligent Systems and Financial Forecasting written by Jason Kingdon and published by Springer. This book was released on 1997-04-28 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the design of an automated system for financial time series forecasting. It explores the level of automation which can be achieved by a system for modelling a given financial time series with the minimum of human intervention. It aims to help the reader understand the issues involved in setting neural network, or genetic algorithm parameters, and to develop methods to deal with the problems they raise in a practical manner. Intelligent Systems and Financial Forecasting will provide invaluable reading material for academic and industrial researchers (particularly those with an interest in the application of adaptive system technology), information technology consultants applying adaptive system techniques, and graduate/postgraduate students in machine learning, AI, business modelling and finance.