Data Visualization
43 curated documents on data visualization from the Gyre Research library, each with a summary. Free to read, no signup required.
Advanced Data Analytics Using Python
Sayan Mukhopadhyay · Book
Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this ...
Also filed under Python, Machine Learning
Advancing into Analytics
George Mount · Book
Foundations of Analytics in Excel 1. Foundations of Exploratory Data Analysis. 3 What Is Exploratory Data Analysis?
Also filed under SQL, Machine Learning
Applied Data Science
Ian Langmore & Daniel Krasner · Book
26 5.2 Coefficient Estimation: Bayesian Formulation . 29 5.2.2 Ideal Gaussian World . 30 5.3 Coefficient Estimation: Optimization Formulation . 33 5.3.1 The least squares problem and the singular value de- composition .
Also filed under Python, Computer Science
Clean Architectures in Python
Leonardo Giordani · Book
6 Prerequisites and structure of the book. 7 Why this book comes for free . 7 Submitting issues or patches. 8 Changes in the second edition.
Also filed under Software Engineering, Python
Data Science: The Hard Parts. Techniques for Excelling at Data Science
Daniel Vaughan · Book
"Data Science: The Hard Parts" by Daniel Vaughan is a comprehensive guide that delves into the more challenging and often overlooked aspects of data science. This book aims to equip readers with the knowledge and techniques required to navigate the complex, real-world challenges faced by data scientists. It goes beyond basic theory and covers essential techniques and strategies that help in tackling the most difficult aspects of data science projects. Key Highlights: Navigating Real-World Data Challenges: Vaughan emphasizes the importance of recognizing and dealing with the inherent complexity of real-world data. He stresses that a large portion of data science is understanding the intricacies and noise in data rather than relying purely on theoretical models. The book explains how to approach messy, incomplete, and imperfect data, which is a significant challenge in most data science tasks. Problem-Solving Framework: The book provides a clear framework for approaching and solving data science problems. It teaches readers how to break down problems into manageable chunks, ensuring that they can tackle complex issues systematically. Vaughan discusses how to structure data science workflows to optimize for success, while keeping the big picture in mind. Exploratory Data Analysis (EDA): The book dives deep into the process of exploratory data analysis, helping readers understand how to approach data, what techniques to use for cleaning and processing, and how to use EDA to find meaningful patterns. It goes beyond basic statistics, introducing methods for identifying hidden structures, correlations, and outliers that may impact model performance. Advanced Machine Learning Techniques: Vaughan covers advanced machine learning techniques, providing insights into more complex models and their real-world applications. The book tackles how to select the appropriate machine learning algorithm for a given problem, optimize model performance, and deal with challenges such as overfitting, underfitting, and bias in data. Model Evaluation and Validation: A significant part of the book is dedicated to model evaluation and validation. Vaughan explores the nuances of cross-validation, model selection, and the process of comparing models to ensure robustness and reliability. He emphasizes the importance of a clear evaluation strategy to avoid common pitfalls such as over-optimism in model performance. Dealing with Ambiguity and Uncertainty: Vaughan recognizes that data science often involves dealing with uncertainty and ambiguity. The book introduces probabilistic thinking and Bayesian techniques, helping readers make informed decisions when faced with incomplete or ambiguous data. This section is particularly useful for those working with predictive modeling and uncertain outcomes. Effective Communication and Presentation: The book highlights the importance of communication in data science. Vaughan discusses how to effectively present complex data insights to non-technical stakeholders, providing guidance on creating clear visualizations and communicating findings in a way that drives decision-making. This includes an emphasis on storytelling with data and visual storytelling. Practical Insights from Real-World Projects: Drawing from Vaughan’s own experience as a data scientist, the book offers real-world case studies and examples that illustrate how the principles discussed can be applied in practice. These examples provide valuable lessons on how to navigate the challenges of data science in different industries.
Also filed under Computer Science, Math
Data-Driven Innovation in the Creative Industries
Melissa Terras, Vikki Jones, Nicola Osborne & Chris Speed · Book
The exploration of innovation in this collection of essays champions the collaborative potential of creativity and technology and encourages the adoption of data-led creativity to shape the future of the creative economy. A must-read for those looking to drive meaningful change and innovation across the industry.” Lee Walters, CEO of Ffilm Cymru Wales. Formerly Programme Manager of Clwstwr, part of the Creative Industries Clusters Programme “This book brings to life real-world applications of data-led creativity and, importantly, addresses ethical considerations. But what do these mean in t...
Also filed under Computer Science, Python
Database Design
Adrienne Watt & Nelson Eng · Book
Database Design - 2nd Edition by Adrienne Watt and Nelson Eng is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted. If you redistribute all or part of this book, you must include the following on the copyright notice page: Download for free from the B.C. Sample APA-style citation: This textbook can be referenced. In APA citation style, it would appear as follows: Watt, A.
Also filed under Software Engineering, SQL
High Performance Python
Micha Gorelick & Ian Ozsvald · Book
Understanding Performant Python. Profiling to Find Bottlenecks.
Also filed under Python, Software Engineering
Introduction to Statistical Concepts
William Astle · Slides
to think, understand, and form judgements logically.
Also filed under Statistics, Risk Management
Learn Python Programming Quickly
Chris Ford · Book
Data Types in Python What Are Data Types? Understanding Data Types Type Casting 5. Lists and Tuples What Is a List Data Type?
Also filed under Python, Machine Learning
Learn SQL Fast
D Armstrong · Book
"Learn SQL Fast: SQL Made Simple! A Beginner's Guide to SQL, with Practical Exercises for Microsoft SQL Server" by D. Armstrong is a comprehensive resource designed to take readers from beginner to proficient in SQL. The book provides a step-by-step approach to learning SQL, focusing on practical application and hands-on exercises. Key Features: Guided Setup: The book begins by assisting readers in acquiring and setting up free SQL Server software and a sample database directly from Microsoft, ensuring a solid foundation for practice. Incremental Learning: SQL concepts are introduced in manageable segments, with each new topic accompanied by clear explanations and examples. This methodical approach facilitates a deeper understanding of each concept before progressing. Hands-On Exercises: At each stage, the book provides practice exercises, allowing readers to apply what they've learned and reinforce their skills through practical application. Progressive Complexity: As the course advances, examples and exercises become more complex, demonstrating how to combine basic concepts to write advanced queries. This progression ensures that readers build upon their knowledge systematically. This book is tailored for individuals who aim to learn SQL efficiently and effectively, providing both the practical skills and the underlying principles necessary for proficient database management.
Also filed under SQL, Machine Learning
Learn SQL Quickly
Code Quickly · Book
"Learn SQL Quickly: A Beginner’s Guide to Learning SQL, Even If You’re New to Databases" is a comprehensive introduction to SQL designed for readers without prior coding experience. Authored by Code Quickly, the book aims to demystify SQL and equip readers with practical skills for managing and manipulating databases. Key Features: Clear and Concise Explanations: The book offers straightforward examples to help readers grasp essential SQL concepts, ensuring a solid foundational understanding. Foundational Knowledge: It provides an in-depth breakdown of what SQL is, making complex topics accessible to beginners. Database Management Setup: Readers are guided through setting up their database management systems, emphasizing a methodical approach to ensure a strong grasp of the basics. Efficient Data Handling: The book teaches how to control data movement effectively, optimizing workflows and minimizing potential issues. Real-World Applications: Incorporating practical examples, the book demonstrates how SQL skills can be applied in real-life scenarios, enhancing learning and retention. Focus on MySQL: It discusses the advantages of using MySQL over other database systems and provides guidance on getting started with it. In today's data-driven world, proficiency in SQL is increasingly valuable. "Learn SQL Quickly" serves as a practical resource for those eager to acquire this in-demand skill efficiently.
Also filed under SQL, Machine Learning
Learning Pandas
Stack Overflow Documentation · Book
Learning Pandas (Stack Overflow Documentation) is a practical and comprehensive guide to the Pandas library, offering a structured approach to data manipulation and analysis in Python. Designed for data analysts, scientists, and developers, this book compiles expert knowledge from Stack Overflow, providing clear explanations and hands-on examples to enhance data processing skills. Key Highlights: Foundational Understanding of Pandas : Introduces Pandas' core data structures, Series and DataFrames, demonstrating their fundamental role in data analysis. Data Importing and Exporting: Covers techniques for reading and writing data in various formats, including CSV, Excel, JSON, and SQL, ensuring seamless integration with different data sources. Data Cleaning and Transformation: Guides readers through handling missing values, filtering, indexing, and data type conversions, essential for preparing datasets for analysis. Efficient Data Manipulation: Explains powerful Pandas functions such as groupby(), merge(), pivot(), and apply(), enabling effective aggregation, reshaping, and computation on datasets. Exploratory Data Analysis (EDA): Discusses summary statistics, value distributions, and data visualization using Pandas, Matplotlib, and Seaborn, aiding in uncovering key insights. Advanced Techniques and Performance Optimization: Introduces time series analysis, vectorization, and efficient memory usage, helping users work with large datasets effectively. Real-World Applications: Provides practical examples and workflows for finance, machine learning preprocessing, and business analytics, making Pandas a valuable tool across industries. With a practical, example-driven approach, this book equips readers with the knowledge to harness the full power of Pandas for data analysis, making complex tasks more manageable and efficient. Whether you’re a beginner or an experienced user, this guide serves as an essential reference for mastering Pandas in Python.
Also filed under Python, Machine Learning
Let Us Python Solutions
Yashavant Kanetkar & Aditya Kanetkar · Book
No part of this publication can be stored in a retrieval system or reproduced in any form or by any means without the prior written permission of the publishers. LIMITS OF LIABILITY AND DISCLAIMER OF WARRANTY The Author and Publisher of this book have tried their best to ensure that the programmes, procedures and functions described in the book are correct. However, the author and the publishers make no warranty of any kind, expressed or implied, with regard to these programmes or the documentation contained in the book. The author and publisher shall not be liable in any event of any damag...
Machine Learning in Python For Everyone
Jonathan Wayna Korn · Book
An introduction to implementing machine learning algorithms in Python using libraries such as scikit-learn. It covers key concepts including data preprocessing, model training, evaluation metrics, and common supervised and unsupervised learning techniques.
Also filed under Machine Learning, Python
MATH-TWS: a package to connect Mathematica to Interactive Brokers Trader Workstation
ALGORITHMIC EXECUTION LLC. · Guide
"MATH-TWS" is a technical guide that introduces a package designed to integrate Mathematica with Interactive Brokers Trader Workstation (TWS), enabling traders and quantitative analysts to develop and execute trading strategies within Mathematica’s powerful computational environment. The book provides insights into the seamless connection between symbolic computation, real-time market data, and algorithmic trading execution. Key Topics Covered: Introduction to Mathematica and Interactive Brokers (IB) API: Explains the fundamentals of using Mathematica for financial modeling and how it can be integrated with IB’s TWS platform. Setting Up MATH-TWS: Guides users through the installation and configuration process to establish a real-time connection between Mathematica and IB’s trading system. Market Data Retrieval: Demonstrates how to fetch live market data, historical data, and financial indicators directly within Mathematica for analysis. Algorithmic Trading Execution: Covers order placement, trade execution, and automated strategy implementation using Mathematica’s computational tools. Risk Management & Portfolio Optimization: Introduces methods to manage trading risk, optimize portfolios, and analyze performance metrics using statistical and machine learning techniques. Customizing & Extending MATH-TWS: Discusses how users can modify and extend the package to fit their specific trading needs. This book is an essential resource for quantitative traders, algorithmic developers, and financial engineers who want to leverage Mathematica’s computational power alongside Interactive Brokers’ API for enhanced trading capabilities. It provides practical coding examples, real-world applications, and a step-by-step approach to building automated trading systems efficiently.
Also filed under Math, Quant Finance
ML Cheatsheet Documentation
Author Unknown · Guide
Warning: If you find errors, please raise an issue or contribute a better definition!
Multivariate GARCH (MGARCH) under Dynamic Conditional Correlation (DCC) specification in Python.
Aaron de la Rosa · Notes
It allows the conditional-on-past-history covariance matrix of the dependent variables to follow a flexible dynamic structure. This means that MGARCH models can capture the time-varying volatility and co-movements in multiple time series. Financial institutions typically use them to estimate the volatility of returns for stocks, bonds, and market indices. DCC (Dynamic Conditional Correlation): DCC is a specific type of MGARCH model.
Also filed under Finance, Risk Management
Papers on Topology
Henri Poincaré · Book
Introduction 1 Topology before Poincar´e . 9 Comments on terminology and notation . Second definition of manifold . Oppositely oriented manifolds .
Also filed under Math, Computer Science
Python 500 Practice Exams Questions & Answers With Explanation
Author Unknown · Guide
Basics of Python Programming Chapter 2. Python Control Structures Chapter 3. Python Functions and Modules Chapter 4. Python Data Structures Chapter 5.
Also filed under Python, Software Engineering
Python Automation Cookbook
Jaime Buelta · Book
"Python Automation Cookbook" by Jaime Buelta is a practical guide that demonstrates how to automate various tasks using Python, aiming to enhance efficiency and productivity. The book employs a problem-solution approach, providing recipes to automate repetitive tasks across different domains. Key Features: Web Scraping and Data Extraction: Learn techniques to scrape websites, detect changes, and extract valuable information for analysis. File and Data Management: Discover methods to search, process, and aggregate raw data files into structured formats like spreadsheets. Report Generation: Explore ways to extract data from Excel spreadsheets and generate comprehensive reports with graphs using libraries such as Matplotlib. Marketing Automation: Understand how to automatically generate marketing campaigns and communicate with recipients over different channels. Debugging Techniques: Gain insights into identifying and implementing precise solutions to common automation challenges. The second edition of the book includes additional chapters focusing on automated code testing, machine learning projects, and handling unstructured data, reflecting Python's growth in data science and AI automation. This cookbook is suitable for developers, data enthusiasts, or anyone interested in automating monotonous manual tasks related to business processes such as finance, sales, and HR. It serves both as a step-by-step guide for beginners and a reference for experienced Python users seeking to streamline their workflows.
Also filed under Python, Machine Learning
Python By Example
Nichola Lacey · Book
"Python By Example" by Nichola Lacey provides a practical approach to learning Python programming through hands-on examples. It guides readers from basic concepts to more advanced topics, using real-world scenarios to illustrate how Python can be applied effectively. The book is designed to help beginners understand and implement Python code by working through a series of projects and exercises.
Also filed under Python, Machine Learning
Python Essentials 1
The OpenEDG Python Institute · Book
About this eBook While ePUB is an open standard widely employed for the publication of electronic books, support and features may vary from one device to another. Every effort has been made to ensure that this book will display faithfully on all devices, but it may be necessary to adjust the settings of your particular device for optimum readability. There are many examples of code employed throughout this book. Due to the flowing nature of text in the ePUB format, this code, which is written line by line, may not always display correctly.
Python Fast/Deep/Simple
Behnam Khani · Book
About the author Hello My name is Behnam Khani. I’m a software engineer with 10 years of experience in the industry. I have a passion for technology, education, and software development and enjoy combining the three. This book and my website dejavucode.com are places to share my knowledge and experiences!
Also filed under Python, Machine Learning
Python for Excel
Felix Zumstein · Book
"Python for Excel" by Felix Zumstein is a comprehensive guide that demonstrates how to integrate Python into Excel workflows, enhancing data analysis and automation capabilities for experienced Excel users. As the creator of xlwings, a popular open-source package for automating Excel with Python, Zumstein provides practical insights and tools to bridge the gap between these two platforms. Key Features: Introduction to Python in Excel: The book begins by introducing Python as a powerful tool for Excel users, emphasizing its advantages over traditional VBA scripting. It guides readers through setting up modern development environments, including Jupyter notebooks and Visual Studio Code, to streamline their workflow. Data Analysis with pandas: Readers learn to utilize the pandas library to acquire, clean, and analyze data, effectively replacing typical Excel calculations with more robust Python operations. This section covers data manipulation techniques essential for efficient analysis. Automation of Excel Tasks: The book provides strategies to automate repetitive tasks such as consolidating workbooks and generating reports. By leveraging Python scripts, users can significantly reduce manual effort and increase productivity. Building Interactive Tools with xlwings: Zumstein delves into using xlwings to create interactive Excel tools that harness Python as a calculation engine. This enables the development of sophisticated applications within the familiar Excel interface. Database Integration and Web Data Retrieval: The guide covers connecting Excel to external databases and importing data from the web using Python, expanding the scope of data sources available for analysis. Unified Tool for Automation and Analysis: By combining Python with Excel, users can replace multiple tools such as VBA, Power Query, and Power Pivot, creating a cohesive and versatile environment for data tasks. For hands-on practice, the author provides a companion repository containing all relevant Jupyter notebooks and code samples, facilitating practical application of the concepts discussed.
Python In easy steps
Mike McGrath · Book
No part of this book may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage or retrieval system, without prior written permission from the publisher. Notice of Liability Every effort has been made to ensure that this book contains accurate and current information. However, In Easy Steps Limited and the author shall not be liable for any loss or damage suffered by readers as a result of any information contained herein. Trademarks All trademarks are acknowledged as belonging to their respective ...
Also filed under Python, Machine Learning
Python in Excel
Hayden Van Der Post · Book
Excel, a stalwart of data manipulation, visualization, and analysis, is ubiquitous in business environments. Python, on the other hand, brings unparalleled versatility and efficiency to data handling tasks. Integrating these two can significantly enhance your data processing capabilities, streamline workflows, and open up new possibilities for advanced analytics. The Foundation: Why Integrate Python with Excel?
Python Machine Learning Workbook for Begginers
AI Publishing · Guide
Introduction and Environment Set Up Data science libraries exist in various programming languages. However, you will be using Python programming language for data science and machine learning since Python is flexible, easy to learn, and offers the most advanced data science and machine learning libraries. Furthermore, Python has a huge data science community from where you can take help whenever you want. In this chapter, you will see how to set up the Python environment needed to run various data science and machine learning libraries.
Also filed under Python, Machine Learning
Python makes programming fun!
Mike McGrath · Book
"Python Makes Programming Fun!" by Mike McGrath is an engaging introduction to Python, designed to make learning to program an enjoyable and accessible experience for beginners. The book provides a step-by-step approach to Python programming, ensuring that readers build a solid foundation in coding while having fun with practical examples and projects. Key Highlights: Beginner-Friendly Approach: The book is designed for those with little or no prior programming experience, making it an excellent resource for students, hobbyists, and aspiring developers who want to explore Python in a simple and engaging way. Core Python Concepts Explained Clearly: Readers are introduced to fundamental programming concepts such as variables, data types, loops, conditional statements, functions, and error handling. Each topic is explained in an easy-to-follow manner with practical examples. Hands-On Coding with Fun Projects: The book encourages hands-on learning by guiding readers through small, enjoyable coding projects. These include creating simple games, automating tasks, and building interactive programs that make learning Python an exciting experience. Graphical and Interactive Programming: In addition to traditional programming exercises, the book covers how to use Python libraries such as Tkinter for GUI development and turtle graphics to create visual projects, making programming more engaging for beginners. Logical Thinking and Problem-Solving Skills: By working through various challenges and exercises, readers develop logical thinking and problem-solving skills that are essential for programming in any language. Python’s Versatility and Real-World Applications: The book highlights Python's broad applications, from web development to data science, showing how learning Python can open doors to various career opportunities. Simple, Practical, and Fun Learning Experience: The author emphasizes a fun and enjoyable learning journey, making programming less intimidating and more rewarding. The use of humor, interactive examples, and step-by-step instructions ensures that readers stay engaged.
Python Mastery
Chloe Annable · Book
"Python Mastery" by Chloe Annable is a comprehensive guide designed to elevate readers from beginner to expert in Python programming. It covers fundamental concepts, advanced techniques, and practical applications, providing hands-on exercises and real-world examples to ensure mastery of the language.
Python Pandas Tutorial For Beginners : The Ultimate Guide For Beginners
Kavi & Shila · Book
A beginner-friendly tutorial on the Pandas library in Python for data manipulation and analysis. It covers fundamental operations such as creating DataFrames, reading CSV files, filtering data, and performing basic statistical summaries.
Also filed under Python, Machine Learning
Python Programming Essentials
Ankit Pandey · Book
Emphasis will also be placed on verifying the installation and resolving potential issues that may arise during the process. Windows Installation To install Python on a Windows operating system, the official Python distribution is recommended. Select the Downloads section and choose the appropria
Also filed under Python, Machine Learning
Python Tkinter
Vaishali B. Bhagat · Book
BHAGAT Copyright © 2024 by Vaishali B. No part of this book may be used or reproduced in any form whatsoever without written permission except in the case of brief quotations in critical articles or reviews.
Also filed under Python, Software Engineering
Representation Learning for Natural Language Processing
Zhiyuan Liu, Yankai Lin & Maosong Sun · Book
Preface In conventional natural language processing (NLP) systems, language items such as words and phrases are handled as distinct symbols. Many classical methods, such as n-gram and bag-of-words models, were proposed and have been widely used until now. All these methods take words as the minimum units for semantic representation, either used to estimate the conditional probabilities of the next word given previous words (e.g., n-gram) or used to represent semantic meanings of text (e.g., bag-of-words models). Even when people find it necessary to model word meanings, they either manually ...
Also filed under AI, Machine Learning
RoadMap Of Mathematics
Hayder Zaeem · Report
4 ○Modules ○Algebras ○Galois Theory: Study of symmetries in algebraic equations. ●Boolean Algebra: Algebraic structure used in logic and computer science.
Also filed under Math, Computer Science
Simple Devops Projects
Author Unknown · Notes
• GitHub - As Distributed version control system. • Jenkins - Continous Integration tool. • Anisible - Configuration Management & Deployment tool. • docker -Containerization • Kubernetes - As Container Management Tool.
Also filed under Software Engineering, SQL
Statistical Analysis Handbook
Michael J de Smith · Book
The moral right of the authors has been asserted. Copies of this edition are available in electronic book and web-accessible formats only. Disclaimer: This publication is designed to offer accurate and authoritative information in regard to the subject matter. It is provided on the understanding that it is not supplied as a form of professional or advisory service.
Also filed under Statistics, Risk Management
Storytelling with data
Cole Nussbaumer Knaflic · Book
A guide based on Cole Nussbaumer Knaflic's influential book on effective data communication. It covers how to choose appropriate visuals, eliminate clutter, and use narrative structure to transform data into compelling stories for business audiences.
Also filed under Python, Statistics
The Analytics Setup Guidebook
Huy Nguyen, Ha Pham, Cedric Chin · Book
"The Analytics Setup Guidebook," authored by Huy Nguyen, Ha Pham, and Cedric Chin, serves as a practical manual for constructing modern, scalable analytics and business intelligence (BI) systems. Targeted primarily at technical team members embarking on setting up analytics infrastructures, the book offers a structured approach to understanding and implementing contemporary data analytics architectures. Key Concepts Covered: High-Level Overview of Analytics Systems: The guidebook introduces the components of modern analytics stacks, emphasizing their interconnections and roles in data processing pipelines. Data Consolidation: It discusses strategies for centralizing data from diverse sources, highlighting best practices in Extract, Load, Transform (ELT) processes and the rationale behind the industry's shift from traditional Extract, Transform, Load (ETL) methods. Data Transformation and Modeling: The book delves into transforming and modeling data to align with business objectives, comparing modern approaches to traditional methodologies and offering insights into efficient data structuring. Data Utilization: It explores the application of data through various means such as ad-hoc reporting, data exploration, and visualization, providing guidance on selecting appropriate BI tools and understanding their roles within the analytics ecosystem.
Think Python
Allen B. Downey · Book
Downey Think Python by Allen B. Downey Copyright © 2024 Allen Downey. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.
Think Stats
Allen B. Downey · Book
"Think Stats" by Allen B. Downey is a practical guide to exploring and analyzing real-world data using Python. It introduces statistical concepts and techniques through hands-on exercises, focusing on understanding data distributions, probability, and statistical inference. The book emphasizes computational thinking and encourages readers to apply statistical methods to solve problems and make data-driven decisions.
Also filed under Statistics, Python
Time Series Analysis
Andrew Lesniewski · Slides
We have seen that a stationary time series in the ARMA(p, q) family can be written in the moving average (MA) form: Xt = µ + εt + γ1εt−1 + γ2εt−2 . = µ + γ(L)εt, (1) where L is the lag operator, and where P∞ j=1 |γj| < ∞. Stationary series are rather unusual in finance, and hence the need for developing models that capture the non-stationary nature of financial time series. There are various approaches to model non-stationarity.
Also filed under Risk Management, Python
Vector Calculus
Michael Corral · Book
About the author: Michael Corral is an Adjunct Faculty member of the Department of Mathematics at Schoolcraft College. in Mathematics from the University of California at Berkeley, and received an M.A. in Industrial & Operations Engineering from the University of Michigan. This text was typeset in LATEX 2ε with the KOMA-Script bundle, using the GNU Emacs text editor on a Fedora Linux system.
Also filed under Math, Quant Finance
The documents are the work of their respective authors and publishers; Gyre Research claims no ownership and will remove any document on request from a rightsholder — team@gyreresearch.com. The summaries and subject classifications are original work by Gyre Research and may be quoted with attribution.