Stat 674: Time Series
Department of Statistics and Biostatistics, CSU East Bay
Spring 2026:
Week 8:
- Due dates: As I discussed in class and have posted on the Assignment page. All of the assignments have listed due dates that are the same as in Canvas. I have added the wording,“or end of finals week.” If you need more time to finish the assignments, you can submit before the end of the semester and I will grade them.
- Project Correction: It has been brought to my attention that the project descriptions had some errors. Project 1 and 3 were suppose to end with the statement, “Write a summary of your findings.” But there were questions from the previous problem related to GDP. I have removed these questions. Project 2 mistakenly had Problem 10 listed, which was an assigned problem in the last homework. Project 2 was supposed to be Problem 11, the next problem. I have made these corrections on the Assignments page.
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
- Frequency Domain:
- Youtube:
Week 6:
- Correction: On the midterm the last question should be use the data though Dec 2017 to forecast the 2018 data.
- 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:
- Wednesday there is a Strike on campus. We will have class online on Zoom on Wednesday this week. The Zoom link is available in Canvas.
- Monday is not a University holiday, we will have class on Monday.
- 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 due this week on Friday.
- 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.
- Quiz: Quiz01 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.
- Google AI Studio App:
- Other R packages with Shiny apps:
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.
- Spotlight R package: DataExplorer AutoEDA
- Gemini Prompt:
I am a Statistics MS Student in a Machine Learning course. We are using R And RStudio for the homework.
The instructor has asked us to start and complete each homework assignment in an R Project. Please make a step by step list for how to create an R Project in R Studio.
- Google AI Tools:
- 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:
- Google AI Studio App:
- ACF cartoon: Allison Horst Allison Horst ACF
Week 1:
- Book using R: fpp3
- Book using Python: fpppy
- 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