Stock Prediction With Deep Learning
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Handbook Of Research On Smart Technology Models For Business And Industry
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Author : Thomas, J. Joshua
language : en
Publisher: IGI Global
Release Date : 2020-06-19
Handbook Of Research On Smart Technology Models For Business And Industry written by Thomas, J. Joshua and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-19 with Technology & Engineering categories.
Advances in machine learning techniques and ever-increasing computing power has helped create a new generation of hardware and software technologies with practical applications for nearly every industry. As the progress has, in turn, excited the interest of venture investors, technology firms, and a growing number of clients, implementing intelligent automation in both physical and information systems has become a must in business. Handbook of Research on Smart Technology Models for Business and Industry is an essential reference source that discusses relevant abstract frameworks and the latest experimental research findings in theory, mathematical models, software applications, and prototypes in the area of smart technologies. Featuring research on topics such as digital security, renewable energy, and intelligence management, this book is ideally designed for machine learning specialists, industrial experts, data scientists, researchers, academicians, students, and business professionals seeking coverage on current smart technology models.
Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes
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Author : Cheng Few Lee
language : en
Publisher: World Scientific
Release Date : 2020-07-30
Handbook Of Financial Econometrics Mathematics Statistics And Machine Learning In 4 Volumes written by Cheng Few Lee and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-30 with Business & Economics categories.
This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.
Machine Learning Solutions
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Author : Jalaj Thanaki
language : en
Publisher:
Release Date : 2018
Machine Learning Solutions written by Jalaj Thanaki and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Software engineering categories.
Practical, hands-on solutions in Python to overcome any problem in Machine Learning About This Book Master the advanced concepts, methodologies, and use cases of machine learning Build ML applications for analytics, NLP and computer vision domains Solve the most common problems in building machine learning models Who This Book Is For This book is for the intermediate users such as machine learning engineers, data engineers, data scientists, and more, who want to solve simple to complex machine learning problems in their day-to-day work and build powerful and efficient machine learning models. A basic understanding of the machine learning concepts and some experience with Python programming is all you need to get started with this book. What You Will Learn Select the right algorithm to derive the best solution in ML domains Perform predictive analysis effciently using ML algorithms Predict stock prices using the stock index value Perform customer analytics for an e-commerce platform Build recommendation engines for various domains Build NLP applications for the health domain Build language generation applications using different NLP techniques Build computer vision applications such as facial emotion recognition In Detail Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You'll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you'll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples. The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions. In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems ...
2021 7th International Conference On Advanced Computing And Communication Systems Icaccs
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Author : IEEE Staff
language : en
Publisher:
Release Date : 2021-03-19
2021 7th International Conference On Advanced Computing And Communication Systems Icaccs written by IEEE Staff and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-19 with categories.
2021 International Conference on Advanced Computing and Communication Systems (ICACCS) aims at exploring the interface between the industry and real time environment with state of the art techniques ICACCS 2021 publishes original and timely research papers and survey articles in current areas of energy, smart city, temperature, power and environment related research areas of current importance to readers
Deep Learning Tools For Predicting Stock Market Movements
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Author : Renuka Sharma
language : en
Publisher: John Wiley & Sons
Release Date : 2024-05-14
Deep Learning Tools For Predicting Stock Market Movements written by Renuka Sharma 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 2024-05-14 with Computers categories.
DEEP LEARNING TOOLS for PREDICTING STOCK MARKET MOVEMENTS The book provides a comprehensive overview of current research and developments in the field of deep learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of deep learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep learning helps foresee market trends with increased accuracy. With advancements in deep learning, new opportunities in styles, tools, and techniques evolve and embrace data-driven insights with theories and practical applications. Learn about designing, training, and applying predictive models with rigorous attention to detail. This book offers critical thinking skills and the cultivation of discerning approaches to market analysis. The book: details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average; explains the rapid expansion of quantum computing technologies in financial systems; provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions; explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers. Audience The book has a wide audience of researchers in financial technology, financial software engineering, artificial intelligence, professional market investors, investment institutions, and asset management companies.
Stock Prediction With Deep Learning
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Author : Ethan Shaotran
language : en
Publisher:
Release Date : 2018-06-10
Stock Prediction With Deep Learning written by Ethan Shaotran and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-10 with categories.
For centuries, human beings have tried to predict the future, whether it be NBA playoffs, weather, or elections. In this book, we tackle the common misconception that the stock market cannot be predicted, and build a stock prediction algorithm to beat the stock market, using Deep Learning, Data Analysis, and Natural Language Processing techniques.If you're new to Artificial Intelligence and Python, and are curious to learn more, this is a great book for you! Industry experts also have plenty to learn from the variety of methods and techniques used in data collection and manipulation.ABOUT THE AUTHOREthan Shaotran is an AI developer, researcher, and author of "Stock Prediction with Deep Learning". He is the founder of Energize.AI, where he built a financial stock prediction algorithm that outperformed the stock market in 2017. He is currently working on a thought experiment series to raise awareness on AI-related societal challenges within the AI community, regarding regulation and potential moral hazards, as well as autonomous vehicle driving software. Ethan has studied Economics and AI courses from Harvard, Stanford, and USF, is an affiliate with the Harvard Kennedy School's AI Initiative and is a member of the Association for Computing Machinery.
Deep Learning Innovations For Securing Critical Infrastructures
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Author : Kumar, Rajeev
language : en
Publisher: IGI Global
Release Date : 2025-04-18
Deep Learning Innovations For Securing Critical Infrastructures written by Kumar, Rajeev and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-18 with Computers categories.
Deep learning innovations play a crucial role in securing critical infrastructures, offering advanced solutions to protect vital systems from sophisticated cyber threats. By leveraging neural networks and advanced algorithms, deep learning enables real-time anomaly detection, pattern recognition, and predictive threat analysis, which are essential for safeguarding critical sectors such as energy, transportation, healthcare, and finance. These technologies can identify vulnerabilities, respond to breaches, and adapt to new attacks, providing a strong defense against cyber risks. As the digital landscape becomes more interconnected, the integration of deep learning into cybersecurity strategies will enhance resilience while ensuring the safe operation of essential services. Deep Learning Innovations for Securing Critical Infrastructures explores the cutting-edge integration of neural networks and artificial intelligence (AI) in modern cybersecurity systems. It examines how AI, particularly neural network models, is revolutionizing cybersecurity by automating threat detection, analyzing complex data patterns, and implementing proactive defense mechanisms. This book covers topics such as blockchain, cloud computing, and event management, and is a useful resource for business owners, computer engineers, data scientists, academicians, and researchers.
Deep Learning Theory And Applications
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Author : Donatello Conte
language : en
Publisher: Springer Nature
Release Date : 2023-07-30
Deep Learning Theory And Applications written by Donatello Conte and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-30 with Computers categories.
This book consitiutes the refereed proceedings of the 4th International Conference on Deep Learning Theory and Applications, DeLTA 2023, held in Rome, Italy from 13 to 14 July 2023. The 9 full papers and 22 short papers presented were thoroughly reviewed and selected from the 42 qualified submissions. The scope of the conference includes such topics as models and algorithms; machine learning; big data analytics; computer vision applications; and natural language understanding.
Mining Data For Financial Applications
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Author : Valerio Bitetta
language : en
Publisher: Springer Nature
Release Date : 2021-01-14
Mining Data For Financial Applications written by Valerio Bitetta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-14 with Computers categories.
This book constitutes revised selected papers from the 5th Workshop on Mining Data for Financial Applications, MIDAS 2020, held in conjunction with ECML PKDD 2020, in Ghent, Belgium, in September 2020.* The 8 full and 3 short papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with challenges, potentialities, and applications of leveraging data-mining tasks regarding problems in the financial domain. *The workshop was held virtually due to the COVID-19 pandemic. “Information Extraction from the GDELT Database to Analyse EU Sovereign Bond Markets” and “Exploring the Predictive Power of News and Neural Machine Learning Models for Economic Forecasting” are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Deep Learning And Other Soft Computing Techniques
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Author : Nguyen Hoang Phuong
language : en
Publisher: Springer Nature
Release Date : 2023-06-26
Deep Learning And Other Soft Computing Techniques written by Nguyen Hoang Phuong and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-26 with Technology & Engineering categories.
This book focuses on the use of artificial intelligence (AI) and computational intelligence (CI) in medical and related applications. Applications include all aspects of medicine: from diagnostics (including analysis of medical images and medical data) to therapeutics (including drug design and radiotherapy) to epidemic- and pandemic-related public health policies. Corresponding techniques include machine learning (especially deep learning), techniques for processing expert knowledge (e.g., fuzzy techniques), and advanced techniques of applied mathematics (such as innovative probabilistic and graph-based techniques). The book also shows that these techniques can be used in many other applications areas, such as finance, transportation, physics. This book helps practitioners and researchers to learn more about AI and CI methods and their biomedical (and related) applications—and to further develop this important research direction.