Stock Market Prediction And Efficiency Analysis Using Recurrent Neural Network
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Stock Market Prediction And Efficiency Analysis Using Recurrent Neural Network
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Author : Joish Bosco
language : en
Publisher:
Release Date : 2018-04-06
Stock Market Prediction And Efficiency Analysis Using Recurrent Neural Network written by Joish Bosco and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-06 with categories.
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.
Deep Learning
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Author : Josh Patterson
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-07-28
Deep Learning written by Josh Patterson and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-28 with Computers categories.
Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool Learn how to use DL4J natively on Spark and Hadoop
Applied Soft Computing And Communication Networks
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Author : Sabu M. Thampi
language : en
Publisher:
Release Date : 2021
Applied Soft Computing And Communication Networks written by Sabu M. Thampi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
This book constitutes thoroughly refereed post-conference proceedings of the International Applied Soft Computing and Communication Networks (ACN 2020) held in VIT, Chennai, India, during October 14-17, 2020. The research papers presented were carefully reviewed and selected from several initial submissions. The book is directed to the researchers and scientists engaged in various fields of intelligent systems.
Introduction To Artificial Neural Systems
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Author : Jacek M. Zurada
language : en
Publisher: Brooks/Cole
Release Date : 1995
Introduction To Artificial Neural Systems written by Jacek M. Zurada and has been published by Brooks/Cole this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Neural networks (Computer science) categories.
Big Data And Machine Learning In Quantitative Investment
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Author : Tony Guida
language : en
Publisher: John Wiley & Sons
Release Date : 2019-03-25
Big Data And Machine Learning In Quantitative Investment written by Tony Guida and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-25 with Business & Economics categories.
Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. • Gain a solid reason to use machine learning • Frame your question using financial markets laws • Know your data • Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how.
Utilizing Ai And Machine Learning In Financial Analysis
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Author : Darwish, Dina
language : en
Publisher: IGI Global
Release Date : 2025-01-21
Utilizing Ai And Machine Learning In Financial Analysis written by Darwish, Dina and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-21 with Business & Economics categories.
Machine learning models can imitate the cognitive process by assimilating knowledge from data and employing it to interpret and analyze information. Machine learning methods facilitate the comprehension of vast amounts of data and reveal significant patterns incorporated within it. This data is utilized to optimize financial business operations, facilitate well-informed judgements, and aid in predictive endeavors. Financial institutions utilize it to enhance pricing, minimize risks stemming from human error, mechanize repetitive duties, and comprehend client behavior. Utilizing AI and Machine Learning in Financial Analysis explores new trends in machine learning and artificial intelligence implementations in the financial sector. It examines techniques in financial analysis using intelligent technologies for improved business services. This book covers topics such as customer relations, predictive analytics, and fraud detection, and is a useful resource for computer engineers, security professionals, business owners, accountants, academicians, data scientists, and researchers.
Metaheuristics In Machine Learning Theory And Applications
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Author : Diego Oliva
language : en
Publisher: Springer Nature
Release Date : 2021-07-13
Metaheuristics In Machine Learning Theory And Applications written by Diego Oliva and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-13 with Computers categories.
This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.
Advanced Security Solutions For Multimedia
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Author : Irshad Ahmad Ansari
language : en
Publisher: IOP Publishing Limited
Release Date : 2021-09-17
Advanced Security Solutions For Multimedia written by Irshad Ahmad Ansari and has been published by IOP Publishing Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-17 with Computers categories.
Modern internet-enabled devices and fast communication technologies have ushered in a revolution in sharing of digital images and video. This may be for social reasons or for commercial and industrial applications, where the data is more likely to include sensitive personal or confidential information. In any event, the shared imagery is intended only for the end-user. Attackers can steal this data or manipulate it for their own uses, causing financial and emotional damage to the owners. Many applications generate important information in the form of images and video, where efficient security is critical. This drives the need for advanced security solutions and the need to continuously develop and maintain security measures in an ever-evolving battle against fraud and malicious intent. There are various techniques employed in protecting digital media and information, such as digital watermarking, cryptography, stenography, data encryption, etc., In addition, sharing platforms and connected nodes themselves may be open to vulnerabilities and can suffer from security breaches. This book reviews present state-of-the-art research related to the security of digital imagery and video, including developments in machine learning applications. It is particularly suited for those that bridge the academic world and industry, and allows readers to understand the security concerns in the multimedia domain by reviewing present and evolving security solutions, their limitations, and future research directions. Key Features Latest trends in the multimedia security domain Includes Machine Learning for multimedia security Insight to different security concerns (attacks) Reviews present challenges & future opportunities Potential & promising solution to the security concerns
Introduction To Time Series Forecasting With Python
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2017-02-16
Introduction To Time Series Forecasting With Python written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-16 with Mathematics categories.
Time series forecasting is different from other machine learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides. In this Ebook, finally cut through the math and specialized methods for time series forecasting. Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.
Icdsmla 2019
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Author : Amit Kumar
language : en
Publisher: Springer Nature
Release Date : 2020-05-19
Icdsmla 2019 written by Amit Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-19 with Technology & Engineering categories.
This book gathers selected high-impact articles from the 1st International Conference on Data Science, Machine Learning & Applications 2019. It highlights the latest developments in the areas of Artificial Intelligence, Machine Learning, Soft Computing, Human–Computer Interaction and various data science & machine learning applications. It brings together scientists and researchers from different universities and industries around the world to showcase a broad range of perspectives, practices and technical expertise.