[Table of Contents] Introduction to Time Series Analysis for Psychology with R and Stan, using Python and PyMC Ver. 5

This article is the table of contents for the series "Introduction to Time Series Analysis for Psychology with R and Stan, using Python and PyMC Ver. 5".

Introduction to "Introduction to Time Series Analysis for Psychology with R and Stan"

The textbook "Introduction to Time Series Analysis for Psychology with R and Stan" was first published in June 2022 and is a wonderful "tutorial"-style introductory book that concisely covers the main themes of time series analysis.

The computer languages used in the textbook are "R" and "Stan". In this
series of articles, I plan to implement the textbook's content by replacing them with "Python" and "PyMC Ver. 5".

Table of Contents / Links

Preface

Chapter 1: Plotting Time Series Data from R Datasets in Python

Chapter 2 Part 1: Practicing Basic R Operations for Time Series Analysis in Python

Chapter 2 Part 2: Practicing Autocorrelation, Decomposition, and ARIMAX in Python

Chapter 3 Part 1: Practicing Unit Roots, Serial Correlation, and Generalized Least Squares (GLS) in Python

Chapter 3 Part 2: Practicing Linear Models (OLS, GLS), Generalized Linear Models, and Linear Mixed Models in Python

Chapter 4 Part 1: State Space Models: Practicing Local Level Models with Python and PyMC Ver. 5

Chapter 4 Part 2: State Space Models with Level, Trend, and Seasonal Components: Practicing with Python and PyMC Ver. 5

Chapter 4 Part 3: Random Intercept Models, Latent Growth Curve Models, and Multivariate Time Series State Space Models: Practicing with Python and PyMC Ver. 5

Chapter 4 Part 4: State Space Models where Observations Follow Binomial and Poisson Distributions: Practicing with Python and PyMC Ver. 5

Chapter 4 Part 5: Change Point Models and Hidden Markov Models in State Space Models: Practicing with Python and PyMC Ver. 5

Chapter 4 Part 6: Time Series Splitting for WAIC Calculation and Cross-Validation: Practicing with Python and PyMC Ver. 5

Chapter 5 Part 1: Practicing Cross-Correlation, Moving Correlation, and Dynamic Time Warping for Two Time Series Datasets in Python

Chapter 5 Part 2: Practicing Coherence, Cross-Wavelet Analysis, Phase Difference, and Rayleigh Test in Python

Chapter 5, Part 3: Practicing Time Series Clustering, Recurrence Quantification Analysis, VAR Models, Granger Causality Tests, and Impulse Response Functions in Python

Chapter 6, Part 1: Practicing Non-negative Matrix Factorization and Dynamic Factor Analysis in Python

Chapter 6, Part 2: Practicing Functional Data Analysis and Functional Principal Component Analysis in Python

Blog Introduction

I am writing eight series of articles on note.
Please come and take a look!

1. Relaxed Statistics

This is a blog that explores probability and statistics roughly, using the Statistical Test Grade 2 problem collection as a guide.
Feel free to read it as if we were just chatting. Please come and take a look.
It corresponds to the CBT-compatible version of the official Statistical Test Grade 2 problem collection.
Sample code for Python and Excel is also available.

2. Connecting with Statistics and Data Analysis

The series 'Connecting with Statistics and Data Analysis' is a blog series that uncovers the 'connections' to statistics and data analysis and presents them in a column format.
With the help of generative AI, I wrote these freely, imagining the entry points to statistics and data analysis.
For example...
- Connections between daily life and statistics
- Connections from Statistical Test Grade 2 to what lies beyond
I hope you enjoy reading them at your leisure 🍀

3. Experiment! Fun Bayesian Modeling 1 & 2 with PyMC Ver. 5

I will draw and analyze the Bayesian models used in psychological research from the books 'Fun Bayesian Modeling' and 'Fun Bayesian Modeling 2' using PyMC Ver. 5.
Like this book, many Bayesian models are written in the R language + Stan.
I will strive to explore the possibilities of PyMC and make Bayesian modeling easy to practice.
Since these are familiar and easy-to-visualize themes, please try running them with PyMC and let's enjoy it together!

4. Experiment! Bayesian Modeling from Iwanami Data Science Vol. 1 with PyMC Ver. 5

I will draw and analyze the Bayesian models by four Bayesians from the book 'Experiment! Iwanami Data Science Vol. 1' using PyMC Ver. 5.
This book is a great resource for learning the basics of Bayesian programming.
I feel like I've become friends with Bayesian methods by happily running PyMC models.
Everyone, please try running them with PyMC, and let's play and learn together!

5. Fun Transcription: Bayesian, Python, etc.

I will blog about the results of my 'book transcription activities' for Bayesian, Python, and others.
I am mainly working on translations into Python.
I hope this will serve as sample code for fellow learners who are also transcribing 🍀

6. Introduction to Time Series Analysis for Psychology with R and Stan, using Python and PyMC Ver. 5

I will practice the time series analysis from the book 'Introduction to Time Series Analysis for Psychology with R and Stan' using Python and PyMC Ver. 5.
This book is packed with time series analysis themes!
I realized the depth of time series analysis.
I will enjoy learning time series analysis with my favorite language, Python.

7. Writing about Data Science-like things

I write columns on statistics, data analysis, AI, machine learning, and Python on an irregular basis.
There are many articles related to statistics and data science books.
Series on 'Statistics', 'Python', 'Mathematics and Python', and 'R' have been born.

8. Notes on Practicing Python Machine Learning Programming

I have written an article about my various thoughts while studying the book "Python Machine Learning: PyTorch & scikit-learn Edition."
This book is a textbook for scikit-learn and PyTorch.
Please feel free to give it a try.

Thank you very much for reading until the end.