Stat 674: Time Series
Department of Statistics and Biostatistics, CSU East Bay
Spring 2025:
Week 8:
- This Week: On Monday March 10 and Wednesday 12 next week there is no class, next week is Finals week for this course. I will hold my usual office hours on Monday and Thursday next week for questions about the Final, the Project or any other assignments you are working on.
- Student Evaluations: Please fill out the student evaluations for the class.
- Meetings:
Week 7:
- Homework: Homework 6 has been posted.
- Final: The Final will be made available on Wednesday that will be due at the end of next week.
- This week we will further discuss ARIMA modeling. We will discuss the application of Neural Networks to time series data.
- Advanced forecasting methods:
- Spotlight paper: Nature: Accurate predictions on small data with a tabular foundation model
- Spotlight paper: arxiv: The Tabular Foundation Model TabPFN Outperforms Specialized Time Series Forecasting Models Based on Simple Features
- website: priorlabs
- Meetings:
- Frequency Domain:
- SonicVisualizer
- Riffussion Uses Stable Diffusion to generate music.
- Fourier Transform
- Youtube:
Week 6:
- Homework: Homework 5 has been posted.
- Download the Author’s slides: Author’s slides We will be using his slides for Regression, Exponential Smoothing, and ARIMA.
- Quarto Notebook: Examples of stock market time series data.
Week 5:
- Quiz: The take-home quiz is due at the end of the week.
- Homework: If you have forgotten to turn in a homework for the class, please submit your homework late for consideration. We will go over homework solutions on Wednesday.
- Homework: Homework 4 has been posted.
- Midterm: The Midterm will be made available on Wednesday that will be due at the end of next week.
- On Wednesday will go over the Quiz and the Midterm.
- On Monday we will continue the discussion about Forecasting models. We will discuss Residual Diagnostics and Prediction Intervals.
- Quarto Notebook:
- On Wednesday we will go over the Chapter 3 and 4 homework related to simple statistics and return to the discussion of forecasting from Chapter 5.
- ACF cartoon: Allison Horst Allison Horst ACF
- Download the Author’s slides: Author’s slides We will be using his slides for Regression, Exponential Smoothing, and ARIMA.
Week 4:
- Homework: Homework 3 has been posted.
- Quiz: Quiz01 has been posted.
- Today we will discuss Time Series Features. In particular the strength of trend \(F_T\) and the strength of seasonality \(F_S\).
- Quarto Notebook:
- Look at the first Forecasting models we will be using in the class.
- Quarto Notebook:
Week 3:
- Homework: Homework 2 has been posted.
- Today we will start with questions about Homework 1. We will discuss the Classical Decomposition and Seasonal Adjustment.
- US Census:
- R package:
- Today we will discuss Time Series Components resulting from the Decomposition of a time series.
Week 2:
- Book: fpp3
- R collections of packages:
- Today we will begin discussing Forecasting and Time Series Analysis. We will begin with some examples and review/introduce the basics of the Tidyverse that are useful for working with tsibble and the other tidyverts R packages. Start by installing the fpp3 R package.
- Examples of time series data.
- Spotlight book: r4ds (2e)
- Spotlight time series software:
- We will be examining many of the datasets from the book in the class. Note that most of the datasets contain more than one time series, so are examples of multivariate time series data.
- Quarto Notebook:
- Examples of time series data. What happened to GameStop Corporation’s stock price during the Covid-19 lock-down. Question: How do we load stock market data into R? This is a good question.
- Time Series plots that show seasonality.
- Quarto Notebook:
- Spotlight Wikipedia:
- On Wednesday we will discuss Autocorrelation, White Noise, and the Moving Average Model MA(1).
- Quarto Notebook:
- ACF cartoon: Allison Horst Allison Horst ACF
Week 1:
- Classroom: NSc112 and Zoom, see Canvas for the link.
- Book: fpp3
- Reference Book: You can access it through the CSUEB Library. Shumway and Stoffer, Time Series: A Data Analysis Approach Using R, CRC Press, 2019.
- Today we will be discussing the syllabus for the class and the material that will be covered.
- Homework: Homework 1 has been posted.
- Spotlight Software:
- Spotlight Blog post: Prime Hints For Running A Data Project In R