Cryptocurrency Market Forecasting With Catboost Models

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Cryptocurrency Market Forecasting With Catboost Models

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Author : Heng Chen
language : en
Publisher: Bentham Science Publishers
Release Date : 2025-05-19

Cryptocurrency Market Forecasting With Catboost Models written by Heng Chen and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-19 with Business & Economics categories.

Cryptocurrency Market Forecasting With Catboost Models explores the intersection of Financial Technology (FinTech) and Big Data Analytics, specifically their impact on cryptocurrency market predictions. It also discusses advanced machine learning techniques, such as the Catboost model, and the evolving landscape of technologies like quantum computing and blockchain. The book outlines the role of digital transformation in finance by introducing FinTech and its evolution. It then covers cryptocurrency market forecasting, addressing its unique challenges and opportunities. Detailed chapters follow the Catboost model, covering its features, practical applications, and advantages. Essential topics include data cleaning, feature engineering, and data preprocessing for robust predictive models. The book also explores big data analytics, distributed computing frameworks, and ethical considerations in AI for market predictions. Real-world case studies illustrate key concepts, concluding with a forward-looking analysis of emerging technologies. Key Features: - Comprehensive coverage of FinTech and its impact on market predictions. - Detailed explanation and practical applications of the Catboost model. - Insights into data preprocessing, feature engineering, and model evaluation. - Exploration of big data analytics and distributed computing in finance. - Discussion of ethical considerations and regulatory challenges in AI for market predictions. - Real-world case studies and examples.

Blockchain Applications In The Smart Era

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Author : Sanjay Misra
language : en
Publisher: Springer Nature
Release Date : 2022-04-19

Blockchain Applications In The Smart Era written by Sanjay Misra and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-19 with Technology & Engineering categories.

This book covers a variety of topics and trends related to blockchain technology for smart era applications. The applications span industries such as health, government, energy management, manufacturing, finance, information systems, all far beyond blockchain's original use in cryptocurrency. The authors present variants, new models, practical solutions, and technological advances related to blockchain in these fields and more. The applications within these fields include blockchain and cyber-security, IoT security and privacy using blockchain, and blockchain in industries and society . A variety of case studies are also included. The book is applicable to researchers, professionals, students, and professors in a variety of fields in communications engineering.

Cryptocurrency Price Analysis Prediction And Forecasting Using Machine Learning With Python

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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-21

Cryptocurrency Price Analysis Prediction And Forecasting Using Machine Learning With Python written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-21 with Computers categories.

In this project, we will be conducting a comprehensive analysis, prediction, and forecasting of cryptocurrency prices using machine learning with Python. The dataset we will be working with contains historical cryptocurrency price data, and our main objective is to build models that can accurately predict future price movements and daily returns. The first step of the project involves exploring the dataset to gain insights into the structure and contents of the data. We will examine the columns, data types, and any missing values present. After that, we will preprocess the data, handling any missing values and converting data types as needed. This will ensure that our data is clean and ready for analysis. Next, we will proceed with visualizing the dataset to understand the trends and patterns in cryptocurrency prices over time. We will create line plots, box plot, violin plot, and other visualizations to study price movements, trading volumes, and volatility across different cryptocurrencies. These visualizations will help us identify any apparent trends or seasonality in the data. To gain a deeper understanding of the time-series nature of the data, we will conduct time-series analysis year-wise and month-wise. This analysis will involve decomposing the time-series into its individual components like trend, seasonality, and noise. Additionally, we will look for patterns in price movements during specific months to identify any recurring seasonal effects. To enhance our predictions, we will also incorporate technical indicators into our analysis. Technical indicators, such as moving averages, Relative Strength Index (RSI), and Moving Average Convergence Divergence (MACD), provide valuable information about price momentum and market trends. These indicators can be used as additional features in our machine learning models. With a strong foundation of data exploration, visualization, and time-series analysis, we will now move on to building machine learning models for forecasting the closing price of cryptocurrencies. We will utilize algorithms like Linear Regression, Support Vector Regression, Random Forest Regression, Decision Tree Regression, K-Nearest Neighbors Regression, Adaboost Regression, Gradient Boosting Regression, Extreme Gradient Boosting Regression, Light Gradient Boosting Regression, Catboost Regression, Multi-Layer Perceptron Regression, Lasso Regression, and Ridge Regression to make forecasting. By training our models on historical data, they will learn to recognize patterns and make predictions for future price movements. As part of our machine learning efforts, we will also develop models for predicting daily returns of cryptocurrencies. Daily returns are essential indicators for investors and traders, as they reflect the percentage change in price from one day to the next. By using historical price data and technical indicators as input features, we can build models that forecast daily returns accurately. Throughout the project, we will perform extensive hyperparameter tuning using techniques like Grid Search and Random Search. This will help us identify the best combinations of hyperparameters for each model, optimizing their performance. To validate the accuracy and robustness of our models, we will use various evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-squared. These metrics will provide insights into the model's ability to predict cryptocurrency prices accurately. In conclusion, this project on cryptocurrency price analysis, prediction, and forecasting is a comprehensive exploration of using machine learning with Python to analyze and predict cryptocurrency price movements. By leveraging data visualization, time-series analysis, technical indicators, and machine learning algorithms, we aim to build accurate and reliable models for predicting future price movements and daily returns. The project's outcomes will be valuable for investors, traders, and analysts looking to make informed decisions in the highly volatile and dynamic world of cryptocurrencies. Through rigorous evaluation and validation, we strive to create robust models that can contribute to a better understanding of cryptocurrency market dynamics and support data-driven decision-making.

Recent Trends In Image Processing And Pattern Recognition

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Author : KC Santosh
language : en
Publisher: Springer Nature
Release Date : 2024-01-29

Recent Trends In Image Processing And Pattern Recognition written by KC Santosh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-29 with Computers categories.

This book constitutes the refereed proceedings of the 6th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2023, held in Derby, UK, during December 2023, in collaboration with the Applied AI Research Lab at the University of South Dakota. The 62 full papers included in this book were carefully reviewed and selected from 216 submissions. The papers are organized in the following topical sections: Volume I: Artificial intelligence and applied machine learning; applied image processing and pattern recognition; and biometrics and applications. Volume II: Healthcare informatics; pattern recognition in blockchain, IOT, cyber plus network security, and cryptography.

Smart Service Systems Operations Management And Analytics

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Author : Hui Yang
language : en
Publisher: Springer Nature
Release Date : 2019-11-25

Smart Service Systems Operations Management And Analytics written by Hui Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-25 with Business & Economics categories.

This volume offers state-of-the-art research in service science and its related research, education and practice areas. It showcases recent developments in smart service systems, operations management and analytics and their impact in complex service systems. The papers included in this volume highlight emerging technology and applications in fields including healthcare, energy, finance, information technology, transportation, sports, logistics, and public services. Regardless of size and service, a service organization is a service system. Because of the socio-technical nature of a service system, a systems approach must be adopted to design, develop, and deliver services, aimed at meeting end users‘ both utilitarian and socio-psychological needs. Effective understanding of service and service systems often requires combining multiple methods to consider how interactions of people, technology, organizations, and information create value under various conditions. The papers in this volume present methods to approach such technical challenges in service science and are based on top papers from the 2019 INFORMS International Conference on Service Science.

Proceedings Of Second International Conference On Sustainable Expert Systems

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Author : Subarna Shakya
language : en
Publisher: Springer Nature
Release Date : 2022-02-26

Proceedings Of Second International Conference On Sustainable Expert Systems written by Subarna Shakya and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-26 with Technology & Engineering categories.

This book features high-quality research papers presented at the 2nd International Conference on Sustainable Expert Systems (ICSES 2021), held in Nepal during September 17–18, 2021. The book focusses on the research information related to artificial intelligence, sustainability, and expert systems applied in almost all the areas of industries, government sectors, and educational institutions worldwide. The main thrust of the book is to publish the conference papers that deal with the design, implementation, development, testing, and management of intelligent and sustainable expert systems and also to provide both theoretical and practical guidelines for the deployment of these systems.

Computer Science And Education

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Author : Wenxing Hong
language : en
Publisher: Springer Nature
Release Date : 2023-05-13

Computer Science And Education written by Wenxing Hong and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-13 with Computers categories.

This three-volume set constitues selected papers presented during the 17th International Conference on Computer Science and Education, ICCSE 2022, held in Ningbo, China, in August 2022. The 168 full papers and 43 short papers presented were thoroughly reviewed and selected from the 510 submissions. They focus on a wide range of computer science topics, especially AI, data science, and engineering, and technology-based education, by addressing frontier technical and business issues essential to the applications of data science in both higher education and advancing e-Society.

Veri Madencili Inde Lojistik Regresyon Modellerinin Ncelenmesi R Uygulamal

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Author : Recep Özsürünç
language : tr
Publisher: Nobel Bilimsel Eserler
Release Date :

Veri Madencili Inde Lojistik Regresyon Modellerinin Ncelenmesi R Uygulamal written by Recep Özsürünç and has been published by Nobel Bilimsel Eserler this book supported file pdf, txt, epub, kindle and other format this book has been release on with Education categories.

A Comparison Of Machine Learning Models For Cryptocurrency Price Prediction

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Author : Dominique Prinz
language : en
Publisher:
Release Date : 2023

A Comparison Of Machine Learning Models For Cryptocurrency Price Prediction written by Dominique Prinz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.

In recent years, there have been advances in methods for time series analysis and prediction. This master’s thesis compares the effectiveness of state-of-the-art machine learningmodels for predicting cryptocurrency prices, considering their accuracy and usability invarious market conditions and for different cryptocurrencies. Utilizing the open-sourceprogramming language Python as a tool to connect to third-party application programming interfaces (APIs), a comprehensive data set is generated. This data set includesvariables that influence prices and serves as the foundational basis for training the models. Furthermore, this thesis also discusses the various model architectures, introduces aframework for time series prediction, and performs statistical analysis and interpretationof the model outcomes. Moreover, it defines and deliberates on the trade-offs betweenmodel performance and complexity. Although this thesis offers a thorough comparisonof cutting-edge machine learning models for cryptocurrency price prediction, it does notdefinitively pinpoint the most promising approach for practical use due to the uniquestrengths and weaknesses of each model. However, by exploring additional features andadjusting the model specifications, the predictive capabilities of these models could beharnessed to assist future investor decision making.

Modeling And Prediction Of Cryptocurrency Prices Using Machine Learning Techniques

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Author : Alireza Ashayer
language : en
Publisher:
Release Date : 2019

Modeling And Prediction Of Cryptocurrency Prices Using Machine Learning Techniques written by Alireza Ashayer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.

With the introduction of Bitcoin in the year 2008 as the first practical decentralized cryptocurrency, the interest in cryptocurrencies and their underlying technology, Blockchain, has skyrocketed. Their promise of security, anonymity, and lack of a central controlling authority make them ideal for users who value their privacy. Academic research on machine learning, Blockchain technology, and their intersection have increased significantly in recent years. Specifically, one of the interest areas for researchers is the possibility of predicting the future prices of these cryptocurrencies using supervised machine learning techniques. In this thesis, we investigate their ability to make one day ahead price prediction of several popular cryptocurrencies using five widely used time-series prediction models. These models are designed by optimizing model parameters, such as activation functions, before settling on the final models presented in this thesis. Finally, we report the performance of each time-series prediction model measured by its mean squared error and accuracy in price movement direction prediction.