Financial Analysis with ARIMA and Time Series Forecasting

Financial Analysis with ARIMA and Time Series Forecasting

by Lazy Programmer
Released July 2024
Publisher(s): Packt Publishing
ISBN: 9781836644231

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Video description

Begin with an introduction to time series analysis, providing a solid foundation for understanding the nature and structure of time series data. You'll explore key concepts such as modeling versus predicting, and learn essential data transformation techniques including power, log, and Box-Cox transformations. These fundamentals set the stage for more advanced topics.

As you delve deeper, you'll encounter a thorough examination of financial time series. You'll learn about random walks, the random walk hypothesis, and the importance of baseline forecasts. The course then transitions to a comprehensive study of ARIMA models. You'll explore autoregressive models (AR), moving average models (MA), and the combination of these in ARIMA. Practical coding sessions will reinforce your understanding, allowing you to apply stationarity tests, ACF, PACF, and Auto ARIMA techniques to real financial data.

The latter part of the course focuses on the application of ARIMA models in forecasting. You'll learn how to implement ARIMA in various scenarios, from stock returns to sales data. The course wraps up with a detailed guide on forecasting out-of-sample data, ensuring you can apply your new skills in real-world situations. Supplementary sections offer guidance on setting up your coding environment and additional help for Python beginners.

What you will learn

  • Understand and analyze time series data
  • Implement data transformations for improved modeling
  • Apply ARIMA models to financial data
  • Perform stationarity tests and utilize ACF/PACF
  • Forecast financial data using ARIMA techniques
  • Develop data-driven decision-making skills

Audience

This course is designed for financial professionals, data analysts, and enthusiasts with a basic understanding of statistics and Python. Prior experience with financial data is beneficial but not required.

About the Author

Lazy Programmer: The Lazy Programmer, a distinguished online educator, boasts dual master's degrees in computer engineering and statistics, with a decade-long specialization in machine learning, pattern recognition, and deep learning, where he authored pioneering courses. His professional journey includes enhancing online advertising and digital media, notably increasing click-through rates and revenue. As a versatile full-stack software engineer, he excels in Python, Ruby on Rails, C++, and more. His expansive knowledge covers areas like bioinformatics and algorithmic trading, showcasing his diverse skill set. Dedicated to simplifying complex topics, he stands as a pivotal figure in online education, adeptly navigating students through the nuances of data science and AI.

Table of contents

  1. Chapter 1 : Welcome
    1. Introduction and Outline
    2. Special Offer
  2. Chapter 2 : Getting Set Up
    1. Warmup (Optional)
    2. Where to get the code
  3. Chapter 3 : Time Series Basics
    1. What is a Time Series?
    2. Modeling vs. Predicting
    3. Power, Log, and Box-Cox Transformations
    4. Suggestion Box (03:10)
  4. Chapter 4 : Financial Basics
    1. Financial Time Series Primer
    2. Random Walks and the Random Walk Hypothesis
    3. The Naive Forecast and the Importance of Baselines
  5. Chapter 5 : ARIMA
    1. ARIMA Section Introduction
    2. Autoregressive Models - AR(p)
    3. Moving Average Models - MA(q)
    4. ARIMA
    5. ARIMA in Code
    6. Stationarity
    7. Stationarity in Code
    8. ACF (Autocorrelation Function)
    9. PACF (Partial Autocorrelation Function)
    10. ACF and PACF in Code (pt 1)
    11. ACF and PACF in Code (pt 2)
    12. Auto ARIMA and SARIMAX
    13. Model Selection, AIC and BIC
    14. Auto ARIMA in Code
    15. Auto ARIMA in Code (Stocks)
    16. ACF and PACF for Stock Returns
    17. Auto ARIMA in Code (Sales Data)
    18. How to Forecast with ARIMA
    19. Forecasting Out-Of-Sample
    20. ARIMA Section Summary
  6. Chapter 6 : Setting Up Your Environment (Appendix)
    1. Pre-Installation Check
    2. Anaconda Environment Setup
    3. How to install Numpy, Scipy, Matplotlib, Pandas, and Tensorflow
  7. Chapter 7 : Extra Help With Python Coding for Beginners (Appendix)
    1. How to Code Yourself (part 1)
    2. How to Code Yourself (part 2)
    3. Proof that using Jupyter Notebook is the same as not using it
    4. How to use Github Extra Coding Tips (Optional)
  8. Chapter 8 : Effective Learning Strategies for Machine Learning (Appendix)
    1. How to Succeed in this Course (Long Version)
    2. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
    3. What order should I take your courses in? (part 1)
    4. What order should I take your courses in? (part 2)

Product information

  • Title: Financial Analysis with ARIMA and Time Series Forecasting
  • Author(s): Lazy Programmer
  • Release date: July 2024
  • Publisher(s): Packt Publishing
  • ISBN: 9781836644231