Time Series and Forecasting
Preface
- Readings and references
- Acknowledgements
I Introduction and Overview
1 Introduction
2 Project workflow
3 Assignment: Write a concept note
II Set up
4 Project set up: Good practices
5 Assignment: Set up your computing environment
III Data Acquisition and Preparation
6 Finding a dataset for your project
- 6.1 Appropriate data sets for time series analysis
- 6.2 Data resources
7 Data acquisition and extraction
8 Assignment: Update your concept and get your data
9 Data preparation strategy
10 Reading in data from source files
- 10.1 First, examine text files in a text editor
- 10.2 Read in CSV files
  - 10.2.1 Declaring data types
11 Data cleaning
- 11.1 Dropping empty rows and columns
12 Transform a data frame to tsibble object
- 12.1 Time indexing
- 12.2 Running diagnostics on your tsibble
13 Saving and loading data files
- 13.1 Fast file reading and writing: The arrow package
14 Assignment: Prepare your data
- 14.1 Generate at least one table or figure
IV Exploratory Data Analysis
15 Exploratory analysis of time series data
16 Time series decomposition
17 Autocorrelation
- 17.1 Heere be monsters
V Model Specification and Estimation
18 The data-generating process
- 18.1 The white noise process
  - 18.1.1 Stationarity
  - 18.1.2 No autocorrelation
19 The normal linear model
- 19.1 Assumptions of the linear model
- 19.2 Examples of the normal linear model
20 Ordinary least squares estimation
21 Assignment: Project proposal
VI Model evaluation
22 Evaluating model residuals
VII Forecasting
23 Producing forecasts
References
Appendix
A Course Syllabus
- A.1 Course Description
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Time Series and Forecasting: A Project-based Approach with R

Time Series and Forecasting: A Project-based Approach with R

Arthur Small

Version of: 2021-03-16

This document is a compilation of class notes for SYS 5581 Time Series and Forecasting, University of Virginia, Spring, 2021.