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syllabus.Rmd
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---
title: "Syllabus"
output:
html_document:
toc: true
toc_depth: 1
toc_float:
collapsed: no
include:
before_body: header.html
---
# Forecasting fisheries catch time series with R (Forenoon)
<img style="float: right" src="images/fish-forecast.jpg" width=20%>
## Topics
- Time-varying regression
- Box-Jenkins (ARMA) Models
- Exponential smoothing
- Modelling time series with seasonality
- Forecast diagnostics and accuracy metrics
# Report-writing and code documentation with R (Afternoon)
<img style="float: right" src="images/tools-logo-transparent.png" width=20%>
## Topics
- Basic workflow using RStudio, Git and GitHub
- Intro to R Markdown
- Creating simple websites from RStudio
- Build an R package with RStudio
- Creating simple websites from R packages on GitHub
- Creating a book with R Markdown: Intro to Bookdown.
- Creating and publishing RShiny applications
# Catch Forecasting Lectures and Labs
<style>
.foo table {
width: 100%
}
.foo td {
width: 50%
}
</style>
**Introduction**
**Time-Varying Regression**
<div class="foo">
Lectures | Labs
------------- | -------------
1 Introduction to time-varying regression | 1 Fit TV regression models to catch data
2 Forecasts with a time-varying regression model | 2 Create time-varying regression forecasts
**ARMA Models**
Lectures | Labs
------------- | -------------
1 Introduction to ARMA Models | 1 Intro to ARMA models and diagnostic plots
2 Stationarity | 2 Test the Greek catch data for stationarity
3 Selecting Model Structure | 3 Fit ARMA Models to the Greek catch data
4 Fitting ARMA Models | 4 Create and test forecasts
5 Create and test forecasts |
**Exponential Smoothing Models**
Lectures | Labs
------------- | -------------
1 Introduction to Exponential Smoothing Models | 1 Fit exponential smoothing models to data
2 Selecting Model Structure | 2 Create forecasts with exponential smoothing models
3 Forecasting with exponential smoothing models | 3 Testing models
**Seasonality**
Lectures | Labs
------------- | -------------
1 Introduction to seasonality and approaches | 1 Creating time-series objects with seasonality in R
2 Seasonal time-vaying regression models | 2 Seasonal exponential smoothing models
3 Seasonal exponential smoothing models |
</div>