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Data Science for Web3

You're reading from   Data Science for Web3 A comprehensive guide to decoding blockchain data with data analysis basics and machine learning cases

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781837637546
Length 344 pages
Edition 1st Edition
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Concepts
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Author (1):
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Gabriela Castillo Areco Gabriela Castillo Areco
Author Profile Icon Gabriela Castillo Areco
Gabriela Castillo Areco
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Toc

Table of Contents (23) Chapters Close

Preface 1. Part 1 Web3 Data Analysis Basics
2. Chapter 1: Where Data and Web3 Meet FREE CHAPTER 3. Chapter 2: Working with On-Chain Data 4. Chapter 3: Working with Off-Chain Data 5. Chapter 4: Exploring the Digital Uniqueness of NFTs – Games, Art, and Identity 6. Chapter 5: Exploring Analytics on DeFi 7. Part 2 Web3 Machine Learning Cases
8. Chapter 6: Preparing and Exploring Our Data 9. Chapter 7: A Primer on Machine Learning and Deep Learning 10. Chapter 8: Sentiment Analysis – NLP and Crypto News 11. Chapter 9: Generative Art for NFTs 12. Chapter 10: A Primer on Security and Fraud Detection 13. Chapter 11: Price Prediction with Time Series 14. Chapter 12: Marketing Discovery with Graphs 15. Part 3 Appendix
16. Chapter 13: Building Experience with Crypto Data – BUIDL 17. Chapter 14: Interviews with Web3 Data Leaders 18. Index 19. Other Books You May Enjoy Appendix 1
1. Appendix 2
2. Appendix 3

Price Prediction with Time Series

A significant amount of time spent by analysts and researchers in the finance industry is devoted to predicting investment opportunities, including asset prices and asset returns. With the availability of large volumes of data and advancements in processing techniques, machine learning (ML) has gained momentum, expanding its application beyond asset pricing to areas such as insurance pricing, portfolio management, and risk management.

In addition to the well-known applications of ML in the financial industry, we can now consider the influence of Web3 and open data. As we have learned throughout this book, data in Web3 is accessible to anyone. Privileged information, such as bank balances or significant account movements, can be viewed by anyone who knows where to look, as explained in Chapter 5.

Many asset modeling and prediction problems in the financial industry involve a time component and the estimation of continuous outputs. This is why...

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