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Time Series and Forecasting
Preface
Readings
Acknowledgements
I Introduction and Overview
1
Introduction
1.1
Course introduction
1.2
The project
1.3
Computing setup
2
Example: A very simple time series analysis
Example problem: Estimating the probability of a weather event
2.1
1. State your question
2.2
2. Acquire data and background information
2.3
3. Organize your data
2.4
4. Perform exploratory analysis of your data
2.5
5. Write down your model of the data generating process
2.5.1
Comments on this statistical model: The risk of model mis-specification
2.6
6. If necessary: transform the model and data to make it ready for analysis
2.7
7. Choose an appropriate technique for estimating model parameters, consistent with your assumptions about your data generating process
2.8
8. Estimate model parameters
2.9
9. Confirm that your modeling assumptions are satisfied
2.10
10. compute measures of model quality; confirm that your model is good enough for your purpose
2.11
11. Use your calibrated model to address your original question.
3
Assignment: Set up computing environment
3.1
The R programming language, and related resources
3.2
Git and Github
3.3
Markdown and R Markdown
3.4
Bibliographic resources: Zotero and Bibtex
3.5
General course web resources
4
Finding and acquiring a dataset for your project
4.1
Appropriate data sets for time series analysis
II Data Preparation
5
Data preparation: Overview
5.1
ETL strategy: Design your end-point data table(s)
5.1.1
Design your end point first (at least, in your head).
5.1.2
Typical structure for a time series data table:
6
Data acquisition and extraction
6.1
Access protocols and permissions
6.2
Accessing databases
6.3
Other comments
7
Reading in data from source files
7.1
First, examine text files in a text editor
7.2
Read in CSV files
7.2.1
Declaring data types
8
Data cleaning
8.1
Dropping empty rows and columns
9
Transform a data frame to
tsibble
object
9.1
Time indexing
9.2
Running diagnostics on your tsibble
9.2.1
Duplicate values
9.2.2
Missing values
9.2.3
Irregular time intervals
10
Saving and loading data files
10.1
Fast file reading and writing: The
arrow
package
Assignment: Extract, Transform, and Load Your Data
10.2
Overview: Extraction, transformation, and loading of data
10.3
Step 1: Identify a time series data set you want to work with
10.4
Step 2: Acquire the data from its source location, reproducibly
10.5
Step 3 Organize your data into a
tidy
data frame
10.6
Step 4: Convert your data into a
tsibble
object
10.7
Step 5: Generate at least one table or figure
10.8
Additional resources and next steps
Announcement: One more week to revise & resubmit data ETL assignment
10.9
References
III Exploratory Data Analysis
11
Exploratory analysis of time series data
11.1
Overview
11.2
Briefly characterize the dataset
11.2.1
Examine subsets of the data
11.3
Plot the time series
11.4
Sesaonal plots
11.4.1
Example: Virginia monthly electricity
11.4.2
Example: Australian production
11.4.3
Seasonal subseries plots
11.5
Scatterplots
11.5.1
A Scatterplot matrix
12
Time series decomposition
12.0.1
Example: Gross Domestic Product data
12.0.2
Fitting data to simple models
12.0.3
Work with ln(GDP)
12.1
Producing forecasts
12.1.1
Model residuals vs. forecast errors
12.1.2
Are the model residuals auto-correlated?
12.2
Example: GDP, several countries
12.2.1
Plot lagged values
13
Autocorrelation
13.1
Heere be monsters
IV Model Specification and Estimation
14
The data-generating process
15
The normal linear model
15.1
Assumptions of the linear model
15.2
Examples of the normal linear model
16
Ordinary least squares estimation
17
Assignment: Project Proposal
17.1
Introduction
17.2
The data and the data-generating process
17.3
Exploratory data analysis
17.4
Plot the time series.
17.5
Perform and report the results of other exploratory data analysis
17.5.1
STL decomposition
17.5.2
Fitting data to simple models
17.5.3
Work with ln(GDP)
17.6
Statistical model
17.6.1
Formal model of data-generating process
17.6.2
Discussion of the statistical model
17.7
Plan for data analysis
17.8
Submission requirements
17.9
Comment
17.10
References
18
(PART) Model evaluation
19
Evaluating model residuals
19.1
Model residuals vs. forecast errors
19.2
Are the model residuals auto-correlated?
19.3
Example: GDP, several models, several countries
20
(PART) Forecasting
20.1
Producing forecasts
References
Appendix
A
Course Syllabus
A.1
Course Description
A.1.1
Prerequisites
A.1.2
Expectations
A.1.3
Readings
A.1.4
Course Objectives
A.1.5
Grading Policy
A.1.6
Attendance Policy
A.1.7
Communications protocols, including emails and office hours
A.1.8
Academic Dishonesty Policy
A.1.9
Disabilities Policy
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Time Series and Forecasting
Chapter 18
(PART) Model evaluation