diff --git a/_freeze/assignments/A1/execute-results/html.json b/_freeze/assignments/A1/execute-results/html.json index 9246af8..044fd29 100644 --- a/_freeze/assignments/A1/execute-results/html.json +++ b/_freeze/assignments/A1/execute-results/html.json @@ -3,9 +3,7 @@ "result": { "engine": "knitr", "markdown": "---\ntitle: Assignment 1\n---\n\nThis assignment will use the same data that you will use in the [retail project](Project.qmd) later in the semester. Each student will use a different time series, selected using their student ID number as follows.\n\n```r\nlibrary(fpp3)\nget_my_data <- function(student_id) {\n set.seed(student_id)\n all_data <- readr::read_rds(\"https://bit.ly/monashretaildata\")\n while(TRUE) {\n retail <- filter(all_data, `Series ID` == sample(`Series ID`, 1))\n if(!any(is.na(fill_gaps(retail)$Turnover))) return(retail)\n }\n}\n# Replace the argument with your student ID\nretail <- get_my_data(12345678)\n```\n\n 1. Plot your time series using the `autoplot()` command. What do you learn from the plot?\n 2. Plot your time series using the `gg_season()` command. What do you learn from the plot?\n 3. Plot your time series using the `gg_subseries()` command. What do you learn from the plot?\n 4. Find an appropriate Box-Cox transformation for your data and explain why you have chosen the particular transformation parameter $\\lambda$.\n 5. Produce a plot of an STL decomposition of the transformed data. What do you learn from the plot?\n\nFor all plots, please use appropriate axis labels and titles.\n\nYou need to submit one Quarto (`qmd`) file which implements all steps above. You may use this file as a starting point.\n\nTo receive full marks, the `qmd` file must compile without errors.\n\n

Due: 28 March 2025
  Submit  
\n", - "supporting": [ - "A1_files" - ], + "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" ], diff --git a/_freeze/assignments/A2/execute-results/html.json b/_freeze/assignments/A2/execute-results/html.json index 39a60e8..ebf342c 100644 --- a/_freeze/assignments/A2/execute-results/html.json +++ b/_freeze/assignments/A2/execute-results/html.json @@ -3,9 +3,7 @@ "result": { "engine": "knitr", "markdown": "---\ntitle: Assignment 2\n---\n\nThis assignment will use the same data that you will use in the [retail project](Project.qmd) later in the semester. Each student will use a different time series, selected using their student ID number as follows.\n\n```r\nlibrary(fpp3)\nget_my_data <- function(student_id) {\n set.seed(student_id)\n all_data <- readr::read_rds(\"https://bit.ly/monashretaildata\")\n while(TRUE) {\n retail <- filter(all_data, `Series ID` == sample(`Series ID`, 1))\n if(!any(is.na(fill_gaps(retail)$Turnover))) return(retail)\n }\n}\n# Replace the argument with your student ID\nretail <- get_my_data(12345678)\n```\n\n1. Using a test set of 2019--2022, fit an ETS model chosen automatically, and three benchmark methods to the training data. Which gives the best forecasts on the test set, based on RMSE?\n2. Check the residuals from the best model using an ACF plot and a Ljung-Box test. Do the residuals appear to be white noise?\n3. Now use time-series cross-validation with a minimum sample size of 15 years, a step size of 1 year, and a forecast horizon of 5 years. Calculate the RMSE of the results. Does it change the conclusion you reach based on the test set?\n4. Which of these two methods of evaluating accuracy is more reliable? Why?\n\nSubmit a Quarto (`qmd`) file which carries out the above analysis. You need to submit one file which implements all steps above. You may use this file as a starting point.\n\nTo receive full marks, the `qmd` file must compile without errors.\n\n

Due: 18 April 2025
  Submit  
\n", - "supporting": [ - "A2_files" - ], + "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" ], diff --git a/_freeze/assignments/A3/execute-results/html.json b/_freeze/assignments/A3/execute-results/html.json index 7407404..ce8e71f 100644 --- a/_freeze/assignments/A3/execute-results/html.json +++ b/_freeze/assignments/A3/execute-results/html.json @@ -3,9 +3,7 @@ "result": { "engine": "knitr", "markdown": "---\ntitle: Assignment 3\n---\n\nThis assignment will use the same data that you will use in the [retail project](Project.qmd) later in the semester. Each student will use a different time series, selected using their student ID number as follows.\n\n```r\nlibrary(fpp3)\nget_my_data <- function(student_id) {\n set.seed(student_id)\n all_data <- readr::read_rds(\"https://bit.ly/monashretaildata\")\n while(TRUE) {\n retail <- filter(all_data, `Series ID` == sample(`Series ID`, 1))\n if(!any(is.na(fill_gaps(retail)$Turnover))) return(retail)\n }\n}\n# Replace the argument with your student ID\nretail <- get_my_data(12345678)\n```\n\nUse a training set up to and including 2018.\n\n* What transformations (Box-Cox and/or differencing) would be required to make the data stationary? You should use a unit-root test as part of the discussion.\n* Use a plot of the ACF and PACF of the (possibly differenced) data to determine two plausible models for this data set.\n* Fit both models, along with an automatically chosen model, and produce forecasts for 2019--2022.\n* Which model is best based on AIC? Which model is best based on the test set RMSE? Which do you think is best to use for future forecasts? Why?\n* Check the residuals from your preferred model, using an ACF plot and a Ljung-Box test. Do the residuals appear to be white noise?\n\nSubmit a Quarto (`qmd`) file which carries out the above analysis. You need to submit one file which implements all steps above. You may use this file as a starting point.\n\n

Due: 16 May 2025
  Submit  
\n", - "supporting": [ - "A3_files" - ], + "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" ], diff --git a/_freeze/assignments/Assignment_template/execute-results/html.json b/_freeze/assignments/Assignment_template/execute-results/html.json index 167b4a7..2e593c4 100644 --- a/_freeze/assignments/Assignment_template/execute-results/html.json +++ b/_freeze/assignments/Assignment_template/execute-results/html.json @@ -3,9 +3,7 @@ "result": { "engine": "knitr", "markdown": "---\ntitle: \"Title of your assignment\"\nauthor: Your Name\nstudent_id: 123456789 # Your student ID\nformat: html\nexecute:\n echo: true\n---\n\n::: {.cell}\n\n```{.r .cell-code}\nlibrary(fpp3)\nget_my_data <- function(student_id) {\n set.seed(student_id)\n all_data <- readr::read_rds(\"https://bit.ly/monashretaildata\")\n while(TRUE) {\n retail <- filter(all_data, `Series ID` == sample(`Series ID`, 1))\n if(!any(is.na(fill_gaps(retail)$Turnover))) return(retail)\n }\n}\nretail <- get_my_data(rmarkdown::metadata$student_id)\n```\n:::\n", - "supporting": [ - "Assignment_template_files" - ], + "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" ], diff --git a/_freeze/week1/index/execute-results/html.json b/_freeze/week1/index/execute-results/html.json index 3dee1b7..39871c3 100644 --- a/_freeze/week1/index/execute-results/html.json +++ b/_freeze/week1/index/execute-results/html.json @@ -1,8 +1,8 @@ { - "hash": "8da1d32c93f7ac64930a30714a9cb8bb", + "hash": "b71c92dee2605ca18bba7260a68f4196", "result": { "engine": "knitr", - "markdown": "---\ntitle: \"Week 1: What is forecasting?\"\n---\n\n::: {.cell}\n\n:::\n\n## What you will learn this week\n\n* How to think about forecasting from a statistical perspective\n* What makes something easy or hard to forecast?\n* Using the `tsibble` package in R\n\n## Pre-class activities\n\n* Install R and RStudio on your personal computer. Instructions are provided at [https://otexts.com/fpp3/appendix-using-r.html](https://otexts.com/fpp3/appendix-using-r.html).\n* Read [Chapter 1 of the textbook](http://otexts.com/fpp3/intro.html) and watch all embedded videos\n* Watch this video\n\n\n\n## Exercises (on your own or in tutorial)\n\nYour task this week is to make sure you are familiar with R, RStudio and the tidyverse packages. If you've already done ETC1010, then you may not need to do anything! But if you're new to R and the tidyverse, then you will need to get yourself up-to-speed.\n\nWork through the first five modules of the **StartR** tutorial at [startr.numbat.space](https://startr.numbat.space). Do as much of it as you think you need. For those students new to R, it is strongly recommended that you do all five modules. For those who have previously used R, concentrate on the parts where you feel you are weakest.\n\n[**Check your understanding quiz**](https://learning.monash.edu/mod/quiz/view.php?id=3444034)\n\n## Slides for seminar\n\n\nDownload pdf\n\n## Seminar activities\n\n\n1. Download `tourism.xlsx` from [`http://robjhyndman.com/data/tourism.xlsx`](http://robjhyndman.com/data/tourism.xlsx), and read it into R using `read_excel()` from the `readxl` package.\n2. Create a tsibble which is identical to the `tourism` tsibble from the `tsibble` package.\n3. Find what combination of `Region` and `Purpose` had the maximum number of overnight trips on average.\n4. Create a new tsibble which combines the Purposes and Regions, and just has total trips by State.\n\n\n\n## Assignments\n\n* [Forecasting Competition](../assignments/competition.qmd) is due on Friday 14 March.\n", + "markdown": "---\ntitle: \"Week 1: What is forecasting?\"\n---\n\n::: {.cell}\n\n:::\n\n## What you will learn this week\n\n* How to think about forecasting from a statistical perspective\n* What makes something easy or hard to forecast?\n* Using the `tsibble` package in R\n\n## Pre-class activities\n\n* Before we start classes, make sure that are familiar with R, RStudio and the tidyverse packages. If you've already done ETC1010, then you may not need to do anything! But if you're new to R and the tidyverse, then you will need to get yourself up-to-speed. Work through the first five modules of the **StartR** tutorial at [startr.numbat.space](https://startr.numbat.space). Do as much of it as you think you need. For those students new to R, it is strongly recommended that you do all five modules. For those who have previously used R, concentrate on the parts where you feel you are weakest.\n\n* Install R and RStudio on your personal computer. Instructions are provided at [https://otexts.com/fpp3/appendix-using-r.html](https://otexts.com/fpp3/appendix-using-r.html).\n\n* Read [Chapter 1 of the textbook](http://otexts.com/fpp3/intro.html) and watch all embedded videos\n\n* Watch this video\n\n\n\n## Slides for Monday lecture\n\n\nDownload pdf\n\n## [Activities for Tuesday workshop](activities.qmd)\n\n## [Check your understanding](https://learning.monash.edu/mod/quiz/view.php?id=3444034)\n\n\n## Assignments\n\n* [Forecasting Competition](../assignments/competition.qmd) is due on Friday 14 March.\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/week1/slides/execute-results/tex.json b/_freeze/week1/slides/execute-results/tex.json index f76b648..e19dac7 100644 --- a/_freeze/week1/slides/execute-results/tex.json +++ b/_freeze/week1/slides/execute-results/tex.json @@ -1,8 +1,8 @@ { - "hash": "3ab9d453403173072aea025fe5c12598", + "hash": "58f2eb33ae52fffdc7131f1f6150559d", "result": { "engine": "knitr", - "markdown": "---\ntitle: ETC3550/ETC5550 Applied forecasting\nauthor: \"Week 1: Introduction to forecasting and R\"\nformat:\n presentation-beamer:\n knitr:\n opts_chunk:\n dev: \"cairo_pdf\"\n pdf-engine: pdflatex\n fig-width: 7.5\n fig-height: 3.5\n include-in-header: ../header.tex\n keep_tex: yes\n---\n\n\n\n## Contact details\n\n\\vspace*{0.2cm}\n\\begin{alertblock}{Chief Examiner: Professor Rob Hyndman}\n\\href{mailto:rob.hyndman@monash.edu}{\\faicon{envelope} rob.hyndman@monash.edu}\n\n\\href{https://robjhyndman.com}{\\faicon{home} robjhyndman.com}\n\\end{alertblock}\n\n\\begin{block}{Tutors}\n\\begin{itemize}\\itemsep=0cm\\parskip=0cm\n\\item \\textbf{Mitchell O'Hara-Wild}\n\\item Elena Sanina\n\\item Zhixiang (Elvis) Yang\n\\item Jarryd Chapman\n\\item Xiefei (Sapphire) Li\n\\item Xiaoqian Wang\n\\end{itemize}\n\\end{block}\n\n## Brief bio\n\\fontsize{13}{16}\\sf\n\n - Professor of Statistics, Monash University\n - Co-author of most popular forecasting textbook in the world\n - Developer of most popular forecasting software in the world\n\n### How my forecasting methodology is used:\n - Pharmaceutical Benefits Scheme\n - Electricity demand\n - Australian tourism demand\n - Ageing population\n - COVID-19 cases\n - TAC large claims\n\n## Unit objectives\n\\fontsize{13}{14}\\sf\n\n 1. To obtain an understanding of common statistical methods used in business and economic forecasting.\n 2. To develop the computer skills required to forecast business and economic time series data;\n 3. To gain insights into the problems of implementing and operating large scale forecasting systems for use in business.\n\n\\pause\n\n### Teaching and learning approach\n\\vspace*{-0.2cm}\n\n* Approximately one hour of online videos each week.\n* One 90 minute in-person tutorial each week.\n* One 50 minute in-person seminar each Friday.\n* One tutorial will be recorded each week and posted online.\n\n## Key reference\n\n\\begin{block}{}\\bf\n\\hangafter=1\\hangindent=.3cm\n {Hyndman, R.~J. \\& Athanasopoulos, G. (2021) \\emph{Forecasting: principles and practice}, 3rd edition}\n\\end{block}\\pause\n\\begin{alertblock}{}\\Large\n\\centerline{\\bf otexts.com/fpp3/}\n\\end{alertblock}\n\n\\pause\n\n * Free and online\n * Data sets in associated R packages\n * R code for examples\n * Embedded online lectures\n\n## Outline\n\n\n\\begin{tabular}[t]{rlr}\n\\textbf{Week} & \\textbf{Topic} & \\textbf{Chapter}\\\\\n\\midrule\n1 & Introduction to forecasting and R & 1\\\\\n2 & Time series graphics & 2\\\\\n3 & Time series decomposition & 3\\\\\n4 & The forecaster's toolbox & 5\\\\\n5--6 & Exponential smoothing & 8\\\\\n7--9 & ARIMA models & 9\\\\\n10 & Multiple regression and forecasting & 7\\\\\n11--12 & Dynamic regression & 10\\\\\n\\end{tabular}\n\n## Assessment\n\\vspace*{-0.2cm}\n\n - Four assignments and one larger project: 40%\n - Exam (2 hours): 60%.\n\n\\pause\n\n\\begin{block}{}\\small\\centerline{\n\\begin{tabular}[t]{llr}\n\\textbf{Task} & \\textbf{Due Date} & \\textbf{Value}\\\\\n\\midrule\nForecasting Competition & Fri 14 Mar & 2\\%\\\\\nAssignment 1 & Fri 28 Mar & 6\\%\\\\\nAssignment 2 & Fri 18 Apr & 6\\%\\\\\nAssignment 3 & Fri 16 May & 6\\%\\\\\nRetail Project & Fri 30 May & 20\\%\\\\\nFinal Exam & Official exam period & 60\\%\\\\\n\\end{tabular}\n\n}\\end{block}\n\n\\pause\\vspace*{-0.4cm}\n\n - Need at least 45\\% for exam, and 50\\% for total.\n - \\textbf{ETC5550 students:} Extra exam question.\n\n## Unit website\n\\fontsize{18}{24}\\sf\\vspace*{0.3cm}\n\n\\begin{alertblock}{}\n\\centerline{\\Large\\textbf{af.numbat.space}}\n\\end{alertblock}\n\n - Includes all course materials\n - Links for assignment submissions\n - Link to discussion forum.\n\n\\vspace*{0.5cm}\n\n###\n\\vspace*{-0.5cm}\n\\centerline{\\textbf{Please don't send emails. Use the forum.}}\n\n## International Institute of Forecasters\n\n\\placefig{1}{3}{width=4cm}{iifLOGO2}\n\n\\begin{textblock}{8}(7,3)\n \\begin{block}{}\n \\begin{itemize}\n \\item The IIF provides a prize to the top student in this subject each year.\n \\item US\\$100 plus one year membership.\n \\end{itemize}\n \\end{block}\n\\end{textblock}\n\n## R\n\\fontsize{13}{13}\\sf\n\n![](figs/Rlogo.png){height=1.3cm}\n\nAvailable for download from CRAN:\n\nhttps://cran.r-project.org\n\n\\vspace*{-0.5cm}\\noindent\\rule{\\textwidth}{1pt}\n\n![](figs/RStudio-Logo-Flat.png){height=1.3cm}\n\nAvailable for download from RStudio:\n\nhttps://www.rstudio.com/products/rstudio/download/\n\n\n\n\n\n## Main packages\n\n\\placefig{4.2}{1.4}{width=3.8cm}{tsibble.png}\n\\placefig{8.0}{1.4}{width=3.8cm}{tsibbledata.png}\n\\placefig{2.3}{4.65}{width=3.8cm}{tidyverse.png}\n\\placefig{6.1}{4.65}{width=3.8cm}{feasts.png}\n\\placefig{9.9}{4.65}{width=3.8cm}{fable.png}\n\n## Main packages\n\n```r\n# Install required packages (do once)\ninstall.packages(c(\"tidyverse\", \"fpp3\", \"GGally), dependencies = TRUE)\n```\n\n\\pause\n\n```r\n# At the start of each session\nlibrary(fpp3)\n```\n\n## Exercises Week 1\n\\fontsize{18}{24}\\sf\n\n * Make sure you are familiar with R, RStudio and the tidyverse packages.\n * Do the first five modules of `startr.numbat.space`.\n * Assignment 1\n\n## Assignment 1: forecast the following series\n\\small\\vspace*{-0.2cm}\n\n 1. Google closing stock price on 20 March 2024\n 2. Maximum temperature at Melbourne airport on 10 April 2024\n 3. The difference in points (Collingwood minus Essendon) scored in the AFL match between Collingwood and Essendon for the Anzac Day clash. 25 April 2024\n 4. The seasonally adjusted estimate of total employment for April 2024. ABS CAT 6202, to be released around mid May 2024\n 5. Google closing stock price on 22 May 2024\n\n\\begin{block}{Due Friday 8 March}\nFor each of these, give a point forecast and an 80\\% prediction interval.\n\\end{block}\\pause\n\\begin{alertblock}{}\nPrize: \\$50 cash prize\n\\end{alertblock}\n\n## Assignment 1: scoring\n\\small\n\n$Y=$ actual, $F=$ point forecast, $[L,U]=$ prediction interval\n\n### Point forecasts:\n\\vspace*{-0.8cm}\n$$\\text{Absolute Error} = |Y-F|\n$$\\vspace*{-1cm}\n\n * Rank results for all students in class\n * Add ranks across all five items\n\n### Prediction intervals:\n\\vspace*{-0.8cm}\n$$\n\\text{Interval Score} = (U - L) + 10(L - Y)_+ + 10 (Y-U)_+\n$$\\vspace*{-1cm}\n\n * $u_+ = \\text{max}(u,0)$\n * Rank results for all students\n * Add ranks across all five items\n\n## `tsibble` objects\n\n\\fontsize{10}{10.8}\\sf\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nglobal_economy\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tsibble: 15,150 x 6 [1Y]\n# Key: Country [263]\n Year Country GDP Imports Exports Population\n \n 1 1960 Afghanistan 537777811. 7.02 4.13 8996351\n 2 1961 Afghanistan 548888896. 8.10 4.45 9166764\n 3 1962 Afghanistan 546666678. 9.35 4.88 9345868\n 4 1963 Afghanistan 751111191. 16.9 9.17 9533954\n 5 1964 Afghanistan 800000044. 18.1 8.89 9731361\n 6 1965 Afghanistan 1006666638. 21.4 11.3 9938414\n 7 1966 Afghanistan 1399999967. 18.6 8.57 10152331\n 8 1967 Afghanistan 1673333418. 14.2 6.77 10372630\n 9 1968 Afghanistan 1373333367. 15.2 8.90 10604346\n10 1969 Afghanistan 1408888922. 15.0 10.1 10854428\n# i 15,140 more rows\n```\n\n\n:::\n:::\n\n\\only<2->{\\begin{textblock}{.75}(1.65,3.4)\n\\begin{alertblock}{}\\fontsize{9}{9}\\sf Index\\phantom{dg}\\end{alertblock}\n\\end{textblock}}\n\\only<3->{\\begin{textblock}{1.6}(2.78,3.4)\n\\begin{alertblock}{}\\fontsize{9}{9}\\sf Key\\phantom{dg}\\end{alertblock}\n\\end{textblock}}\n\\only<4>{\\begin{textblock}{6.7}(4.8,3.4)\n\\begin{alertblock}{}\\fontsize{9}{9}\\sf Measured variables\\phantom{dg}\\end{alertblock}\n\\end{textblock}}\n\n## `tsibble` objects\n\n\\fontsize{10}{10.8}\\sf\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ntourism\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tsibble: 24,320 x 5 [1Q]\n# Key: Region, State, Purpose [304]\n Quarter Region State Purpose Trips\n \n 1 1998 Q1 Adelaide SA Business 135.\n 2 1998 Q2 Adelaide SA Business 110.\n 3 1998 Q3 Adelaide SA Business 166.\n 4 1998 Q4 Adelaide SA Business 127.\n 5 1999 Q1 Adelaide SA Business 137.\n 6 1999 Q2 Adelaide SA Business 200.\n 7 1999 Q3 Adelaide SA Business 169.\n 8 1999 Q4 Adelaide SA Business 134.\n 9 2000 Q1 Adelaide SA Business 154.\n10 2000 Q2 Adelaide SA Business 169.\n# i 24,310 more rows\n```\n\n\n:::\n:::\n\n\\only<3->{\\begin{textblock}{.98}(1.65,3.37)\n\\begin{alertblock}{}\\fontsize{9}{9}\\sf Index\\phantom{dg}\\end{alertblock}\n\\end{textblock}}\n\\only<4->{\\begin{textblock}{3.9}(3,3.37)\n\\begin{alertblock}{}\\fontsize{9}{9}\\sf Keys\\phantom{dg}\\end{alertblock}\n\\end{textblock}}\n\\only<5>{\\begin{textblock}{1.5}(7.4,3.37)\n\\begin{alertblock}{}\\fontsize{9}{9}\\sf Measure\\phantom{dg}\\end{alertblock}\n\\end{textblock}}\n\n\\only<2->{\\begin{textblock}{3}(9,5)\n\\begin{block}{}\\fontsize{10}{10}\\sf Domestic visitor nights in thousands by state/region and purpose.\\phantom{dg}\\end{block}\n\\end{textblock}}\n\n## `tsibble` objects\n\n* A `tsibble` allows storage and manipulation of multiple time series in R.\n\n* It contains:\n\n + An index: time information about the observation\n + Measured variable(s): numbers of interest\n + Key variable(s): optional unique identifiers for each series\n\n* It works with tidyverse functions.\n\n## The `tsibble` index\n\nTime index variables can be created with these functions:\n\n###\n\\vspace*{-0.2cm}\n\n\n\\begin{tabular}[t]{ll}\n\\textbf{Frequency} & \\textbf{Function}\\\\\n\\midrule\nAnnual & \\texttt{start:end}\\\\\nQuarterly & \\texttt{yearquarter()}\\\\\nMonthly & \\texttt{yearmonth()}\\\\\nWeekly & \\texttt{yearweek()}\\\\\nDaily & \\texttt{as\\_date()}, \\texttt{ymd()}\\\\\nSub-daily & \\texttt{as\\_datetime()}\\\\\n\\end{tabular}\n\n\n## Your turn\n\n\n1. Download `tourism.xlsx` from [`http://robjhyndman.com/data/tourism.xlsx`](http://robjhyndman.com/data/tourism.xlsx), and read it into R using `read_excel()` from the `readxl` package.\n2. Create a tsibble which is identical to the `tourism` tsibble from the `tsibble` package.\n3. Find what combination of `Region` and `Purpose` had the maximum number of overnight trips on average.\n4. Create a new tsibble which combines the Purposes and Regions, and just has total trips by State.\n", + "markdown": "---\ntitle: ETC3550/ETC5550 Applied forecasting\nauthor: \"Week 1: Introduction to forecasting and R\"\nformat:\n presentation-beamer:\n knitr:\n opts_chunk:\n dev: \"cairo_pdf\"\n pdf-engine: pdflatex\n fig-width: 7.5\n fig-height: 3.5\n include-in-header: ../header.tex\n keep_tex: yes\n---\n\n\n\n## Contact details\n\n\\vspace*{0.2cm}\n\\begin{alertblock}{Chief Examiner: Professor Rob Hyndman}\n\\href{mailto:rob.hyndman@monash.edu}{\\faicon{envelope} rob.hyndman@monash.edu}\n\n\\href{https://robjhyndman.com}{\\faicon{home} robjhyndman.com}\n\\end{alertblock}\n\n\\begin{block}{Tutors}\n\\begin{itemize}\\itemsep=0cm\\parskip=0cm\n\\item \\textbf{Mitchell O'Hara-Wild}\n\\item Elena Sanina\n\\item Zhixiang (Elvis) Yang\n\\item Jarryd Chapman\n\\item Xiefei (Sapphire) Li\n\\item Xiaoqian Wang\n\\end{itemize}\n\\end{block}\n\n## Brief bio\n\\fontsize{13}{16}\\sf\n\n - Professor of Statistics, Monash University\n - Co-author of most popular forecasting textbook in the world\n - Developer of most popular forecasting software in the world\n\n### How my forecasting methodology is used:\n - Pharmaceutical Benefits Scheme\n - Electricity demand\n - Australian tourism demand\n - Ageing population\n - COVID-19 cases\n - TAC large claims\n\n## Unit objectives\n\\fontsize{13}{14}\\sf\n\n 1. To obtain an understanding of common statistical methods used in business and economic forecasting.\n 2. To develop the computer skills required to forecast business and economic time series data;\n 3. To gain insights into the problems of implementing and operating large scale forecasting systems for use in business.\n\n\\pause\n\n### Teaching and learning approach\n\\vspace*{-0.2cm}\n\n* Approximately one hour of online videos each week.\n* One 90 minute in-person tutorial each week.\n* One 50 minute in-person seminar each Friday.\n* One tutorial will be recorded each week and posted online.\n\n## Key reference\n\n\\begin{block}{}\\bf\n\\hangafter=1\\hangindent=.3cm\n {Hyndman, R.~J. \\& Athanasopoulos, G. (2021) \\emph{Forecasting: principles and practice}, 3rd edition}\n\\end{block}\\pause\n\\begin{alertblock}{}\\Large\n\\centerline{\\bf otexts.com/fpp3/}\n\\end{alertblock}\n\n\\pause\n\n * Free and online\n * Data sets in associated R packages\n * R code for examples\n * Embedded online lectures\n\n## Outline\n\n\n\\begin{tabular}[t]{rlr}\n\\textbf{Week} & \\textbf{Topic} & \\textbf{Chapter}\\\\\n\\midrule\n1 & Introduction to forecasting and R & 1\\\\\n2 & Time series graphics & 2\\\\\n3 & Time series decomposition & 3\\\\\n4 & The forecaster's toolbox & 5\\\\\n5--6 & Exponential smoothing & 8\\\\\n7--9 & ARIMA models & 9\\\\\n10 & Multiple regression and forecasting & 7\\\\\n11--12 & Dynamic regression & 10\\\\\n\\end{tabular}\n\n## Assessment\n\\vspace*{-0.2cm}\n\n - Four assignments and one larger project: 40%\n - Exam (2 hours): 60%.\n\n\\pause\n\n\\begin{block}{}\\small\\centerline{\n\\begin{tabular}[t]{llr}\n\\textbf{Task} & \\textbf{Due Date} & \\textbf{Value}\\\\\n\\midrule\nForecasting Competition & Fri 14 Mar & 2\\%\\\\\nAssignment 1 & Fri 28 Mar & 6\\%\\\\\nAssignment 2 & Fri 18 Apr & 6\\%\\\\\nAssignment 3 & Fri 16 May & 6\\%\\\\\nRetail Project & Fri 30 May & 20\\%\\\\\nFinal Exam & Official exam period & 60\\%\\\\\n\\end{tabular}\n\n}\\end{block}\n\n\\pause\\vspace*{-0.4cm}\n\n - Need at least 45\\% for exam, and 50\\% for total.\n - \\textbf{ETC5550 students:} Extra exam question.\n\n## Unit website\n\\fontsize{18}{24}\\sf\\vspace*{0.3cm}\n\n\\begin{alertblock}{}\n\\centerline{\\Large\\textbf{af.numbat.space}}\n\\end{alertblock}\n\n - Includes all course materials\n - Links for assignment submissions\n - Link to discussion forum.\n\n\\vspace*{0.5cm}\n\n###\n\\vspace*{-0.5cm}\n\\centerline{\\textbf{Please don't send emails. Use the forum.}}\n\n## International Institute of Forecasters\n\n\\placefig{1}{3}{width=4cm}{iifLOGO2}\n\n\\begin{textblock}{8}(7,3)\n \\begin{block}{}\n \\begin{itemize}\n \\item The IIF provides a prize to the top student in this subject each year.\n \\item US\\$100 plus one year membership.\n \\end{itemize}\n \\end{block}\n\\end{textblock}\n\n## R\n\\fontsize{13}{13}\\sf\n\n![](figs/Rlogo.png){height=1.3cm}\n\nAvailable for download from CRAN:\n\nhttps://cran.r-project.org\n\n\\vspace*{-0.5cm}\\noindent\\rule{\\textwidth}{1pt}\n\n![](figs/RStudio-Logo-Flat.png){height=1.3cm}\n\nAvailable for download from RStudio:\n\nhttps://www.rstudio.com/products/rstudio/download/\n\n\n\n\n\n## Main packages\n\n\\placefig{4.2}{1.4}{width=3.8cm}{tsibble.png}\n\\placefig{8.0}{1.4}{width=3.8cm}{tsibbledata.png}\n\\placefig{2.3}{4.65}{width=3.8cm}{tidyverse.png}\n\\placefig{6.1}{4.65}{width=3.8cm}{feasts.png}\n\\placefig{9.9}{4.65}{width=3.8cm}{fable.png}\n\n## Main packages\n\n```r\n# Install required packages (do once)\ninstall.packages(c(\"tidyverse\", \"fpp3\", \"GGally), dependencies = TRUE)\n```\n\n\\pause\n\n```r\n# At the start of each session\nlibrary(fpp3)\n```\n\n## Exercises Week 1\n\\fontsize{18}{24}\\sf\n\n * Make sure you are familiar with R, RStudio and the tidyverse packages.\n * Do the first five modules of `startr.numbat.space`.\n * Assignment 1\n\n## Assignment 1: forecast the following series\n\\small\\vspace*{-0.2cm}\n\n 1. Google closing stock price on 20 March 2024\n 2. Maximum temperature at Melbourne airport on 10 April 2024\n 3. The difference in points (Collingwood minus Essendon) scored in the AFL match between Collingwood and Essendon for the Anzac Day clash. 25 April 2024\n 4. The seasonally adjusted estimate of total employment for April 2024. ABS CAT 6202, to be released around mid May 2024\n 5. Google closing stock price on 22 May 2024\n\n\\begin{block}{Due Friday 8 March}\nFor each of these, give a point forecast and an 80\\% prediction interval.\n\\end{block}\\pause\n\\begin{alertblock}{}\nPrize: \\$50 cash prize\n\\end{alertblock}\n\n## Assignment 1: scoring\n\\small\n\n$Y=$ actual, $F=$ point forecast, $[L,U]=$ prediction interval\n\n### Point forecasts:\n\\vspace*{-0.8cm}\n$$\\text{Absolute Error} = |Y-F|\n$$\\vspace*{-1cm}\n\n * Rank results for all students in class\n * Add ranks across all five items\n\n### Prediction intervals:\n\\vspace*{-0.8cm}\n$$\n\\text{Interval Score} = (U - L) + 10(L - Y)_+ + 10 (Y-U)_+\n$$\\vspace*{-1cm}\n\n * $u_+ = \\text{max}(u,0)$\n * Rank results for all students\n * Add ranks across all five items\n\n## `tsibble` objects\n\n\\fontsize{10}{10.8}\\sf\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\nglobal_economy\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tsibble: 15,150 x 6 [1Y]\n# Key: Country [263]\n Year Country GDP Imports Exports Population\n \n 1 1960 Afghanistan 537777811. 7.02 4.13 8996351\n 2 1961 Afghanistan 548888896. 8.10 4.45 9166764\n 3 1962 Afghanistan 546666678. 9.35 4.88 9345868\n 4 1963 Afghanistan 751111191. 16.9 9.17 9533954\n 5 1964 Afghanistan 800000044. 18.1 8.89 9731361\n 6 1965 Afghanistan 1006666638. 21.4 11.3 9938414\n 7 1966 Afghanistan 1399999967. 18.6 8.57 10152331\n 8 1967 Afghanistan 1673333418. 14.2 6.77 10372630\n 9 1968 Afghanistan 1373333367. 15.2 8.90 10604346\n10 1969 Afghanistan 1408888922. 15.0 10.1 10854428\n# i 15,140 more rows\n```\n\n\n:::\n:::\n\n\\only<2->{\\begin{textblock}{.75}(1.65,3.4)\n\\begin{alertblock}{}\\fontsize{9}{9}\\sf Index\\phantom{dg}\\end{alertblock}\n\\end{textblock}}\n\\only<3->{\\begin{textblock}{1.6}(2.78,3.4)\n\\begin{alertblock}{}\\fontsize{9}{9}\\sf Key\\phantom{dg}\\end{alertblock}\n\\end{textblock}}\n\\only<4>{\\begin{textblock}{6.7}(4.8,3.4)\n\\begin{alertblock}{}\\fontsize{9}{9}\\sf Measured variables\\phantom{dg}\\end{alertblock}\n\\end{textblock}}\n\n## `tsibble` objects\n\n\\fontsize{10}{10.8}\\sf\n\n::: {.cell}\n\n:::\n\n::: {.cell}\n\n```{.r .cell-code}\ntourism\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tsibble: 24,320 x 5 [1Q]\n# Key: Region, State, Purpose [304]\n Quarter Region State Purpose Trips\n \n 1 1998 Q1 Adelaide SA Business 135.\n 2 1998 Q2 Adelaide SA Business 110.\n 3 1998 Q3 Adelaide SA Business 166.\n 4 1998 Q4 Adelaide SA Business 127.\n 5 1999 Q1 Adelaide SA Business 137.\n 6 1999 Q2 Adelaide SA Business 200.\n 7 1999 Q3 Adelaide SA Business 169.\n 8 1999 Q4 Adelaide SA Business 134.\n 9 2000 Q1 Adelaide SA Business 154.\n10 2000 Q2 Adelaide SA Business 169.\n# i 24,310 more rows\n```\n\n\n:::\n:::\n\n\\only<3->{\\begin{textblock}{.98}(1.65,3.37)\n\\begin{alertblock}{}\\fontsize{9}{9}\\sf Index\\phantom{dg}\\end{alertblock}\n\\end{textblock}}\n\\only<4->{\\begin{textblock}{3.9}(3,3.37)\n\\begin{alertblock}{}\\fontsize{9}{9}\\sf Keys\\phantom{dg}\\end{alertblock}\n\\end{textblock}}\n\\only<5>{\\begin{textblock}{1.5}(7.4,3.37)\n\\begin{alertblock}{}\\fontsize{9}{9}\\sf Measure\\phantom{dg}\\end{alertblock}\n\\end{textblock}}\n\n\\only<2->{\\begin{textblock}{3}(9,5)\n\\begin{block}{}\\fontsize{10}{10}\\sf Domestic visitor nights in thousands by state/region and purpose.\\phantom{dg}\\end{block}\n\\end{textblock}}\n\n## `tsibble` objects\n\n* A `tsibble` allows storage and manipulation of multiple time series in R.\n\n* It contains:\n\n + An index: time information about the observation\n + Measured variable(s): numbers of interest\n + Key variable(s): optional unique identifiers for each series\n\n* It works with tidyverse functions.\n\n## The `tsibble` index\n\nTime index variables can be created with these functions:\n\n###\n\\vspace*{-0.2cm}\n\n\n\\begin{tabular}[t]{ll}\n\\textbf{Frequency} & \\textbf{Function}\\\\\n\\midrule\nAnnual & \\texttt{start:end}\\\\\nQuarterly & \\texttt{yearquarter()}\\\\\nMonthly & \\texttt{yearmonth()}\\\\\nWeekly & \\texttt{yearweek()}\\\\\nDaily & \\texttt{as\\_date()}, \\texttt{ymd()}\\\\\nSub-daily & \\texttt{as\\_datetime()}\\\\\n\\end{tabular}\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/week10/slides/figure-beamer/traintest1-1.pdf b/_freeze/week10/slides/figure-beamer/traintest1-1.pdf index 4118ef4..e792777 100644 Binary files a/_freeze/week10/slides/figure-beamer/traintest1-1.pdf and b/_freeze/week10/slides/figure-beamer/traintest1-1.pdf differ diff --git a/_freeze/week10/slides/figure-beamer/traintest1a-1.pdf b/_freeze/week10/slides/figure-beamer/traintest1a-1.pdf index 666d3f6..e8f0a4f 100644 Binary files a/_freeze/week10/slides/figure-beamer/traintest1a-1.pdf and b/_freeze/week10/slides/figure-beamer/traintest1a-1.pdf differ diff --git a/_freeze/week10/slides/figure-beamer/tscvggplot1-1.pdf b/_freeze/week10/slides/figure-beamer/tscvggplot1-1.pdf index 9ed102d..e76275c 100644 Binary files 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include-in-header: ../header.tex\n keep_tex: yes\n---\n\n\n\n## Your turn\n\\fontsize{13}{14}\\sf\n\n\n\n\n## The ABS stuff-up\n\n\\fullheight{abs1}\n\n## The ABS stuff-up\n\n\\fullheight{abs2}\n\n## The ABS stuff-up\n\n\\fullheight{abs3}\n\n## The ABS stuff-up\n\n::: {.cell}\n\n:::\n\n\\fontsize{10}{10}\\sf\n\n::: {.cell}\n\n```{.r .cell-code}\nemployed\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tsibble: 440 x 4 [1M]\n Time Month Year Employed\n \n 1 1978 Feb Feb 1978 5986.\n 2 1978 Mar Mar 1978 6041.\n 3 1978 Apr Apr 1978 6054.\n 4 1978 May May 1978 6038.\n 5 1978 Jun Jun 1978 6031.\n 6 1978 Jul Jul 1978 6036.\n 7 1978 Aug Aug 1978 6005.\n 8 1978 Sep Sep 1978 6024.\n 9 1978 Oct Oct 1978 6046.\n10 1978 Nov Nov 1978 6034.\n# i 430 more rows\n```\n\n\n:::\n:::\n\n## The ABS stuff-up\n\n::: {.cell}\n\n```{.r .cell-code}\nemployed |>\n autoplot(Employed) +\n labs(title = \"Total employed\", y = \"Thousands\")\n```\n\n::: {.cell-output-display}\n![](slides_files/figure-beamer/abs3-1.pdf){fig-pos='H'}\n:::\n:::\n\n## The ABS stuff-up\n\n::: {.cell}\n\n```{.r .cell-code}\nemployed |>\n filter(Year >= 2005) |>\n autoplot(Employed) +\n labs(title = \"Total employed\", y = \"Thousands\")\n```\n\n::: {.cell-output-display}\n![](slides_files/figure-beamer/abs4-1.pdf){fig-pos='H'}\n:::\n:::\n\n## The ABS stuff-up\n\n::: {.cell}\n\n```{.r .cell-code}\nemployed |>\n filter(Year >= 2005) |>\n gg_season(Employed, labels = \"right\") +\n labs(title = \"Total employed\", y = \"Thousands\")\n```\n\n::: {.cell-output-display}\n![](slides_files/figure-beamer/abs5-1.pdf){fig-pos='H'}\n:::\n:::\n\n## The ABS stuff-up\n\n::: {.cell}\n\n```{.r .cell-code}\nemployed |>\n mutate(diff = difference(Employed)) |>\n filter(Month == \"Sep\") |>\n ggplot(aes(y = diff, x = 1)) +\n geom_boxplot() +\n coord_flip() +\n labs(title = \"Sep - Aug: total employed\", y = \"Thousands\") +\n scale_x_continuous(breaks = NULL, labels = NULL)\n```\n\n::: {.cell-output-display}\n![](slides_files/figure-beamer/abs6-1.pdf){fig-pos='H'}\n:::\n:::\n\n## The ABS stuff-up\n\n::: {.cell}\n\n```{.r .cell-code}\ndcmp <- employed |>\n filter(Year >= 2005) |>\n model(stl = STL(Employed ~ season(window = 11), robust = TRUE))\ncomponents(dcmp) |> autoplot()\n```\n\n::: {.cell-output-display}\n![](slides_files/figure-beamer/abs7-1.pdf){fig-pos='H'}\n:::\n:::\n\n## The ABS stuff-up\n\n::: {.cell}\n\n```{.r .cell-code}\ncomponents(dcmp) |>\n filter(year(Time) == 2013) |>\n gg_season(season_year) +\n labs(title = \"Seasonal component\") + guides(colour = \"none\")\n```\n\n::: {.cell-output-display}\n![](slides_files/figure-beamer/abs8-1.pdf){fig-pos='H'}\n:::\n:::\n\n## The ABS stuff-up\n\n::: {.cell}\n\n```{.r .cell-code}\ncomponents(dcmp) |>\n as_tsibble() |>\n autoplot(season_adjust)\n```\n\n::: {.cell-output-display}\n![](slides_files/figure-beamer/abs9-1.pdf){fig-pos='H'}\n:::\n:::\n\n## The ABS stuff-up\n\n * August 2014 employment numbers higher than expected.\n * Supplementary survey usually conducted in August for employed people.\n * Most likely, some employed people were claiming to be unemployed in August to avoid supplementary questions.\n * Supplementary survey not run in 2014, so no motivation to lie about employment.\n * In previous years, seasonal adjustment fixed the problem.\n * The ABS has now adopted a new method to avoid the bias.\n", + "markdown": "---\ntitle: ETC3550/ETC5550 Applied forecasting\nauthor: \"Week 3: Time series decomposition\"\nformat:\n presentation-beamer:\n knitr:\n opts_chunk:\n dev: \"cairo_pdf\"\n pdf-engine: pdflatex\n fig-width: 7.5\n fig-height: 3.5\n include-in-header: ../header.tex\n keep_tex: yes\n---\n\n\n\n## The ABS stuff-up\n\n\\fullheight{abs1}\n\n## The ABS stuff-up\n\n\\fullheight{abs2}\n\n## The ABS stuff-up\n\n\\fullheight{abs3}\n\n## The ABS stuff-up\n\n::: {.cell}\n\n:::\n\n\\fontsize{10}{10}\\sf\n\n::: {.cell}\n\n```{.r .cell-code}\nemployed\n```\n\n::: {.cell-output .cell-output-stdout}\n\n```\n# A tsibble: 440 x 4 [1M]\n Time Month Year Employed\n \n 1 1978 Feb Feb 1978 5986.\n 2 1978 Mar Mar 1978 6041.\n 3 1978 Apr Apr 1978 6054.\n 4 1978 May May 1978 6038.\n 5 1978 Jun Jun 1978 6031.\n 6 1978 Jul Jul 1978 6036.\n 7 1978 Aug Aug 1978 6005.\n 8 1978 Sep Sep 1978 6024.\n 9 1978 Oct Oct 1978 6046.\n10 1978 Nov Nov 1978 6034.\n# i 430 more rows\n```\n\n\n:::\n:::\n\n## The ABS stuff-up\n\n::: {.cell}\n\n```{.r .cell-code}\nemployed |>\n autoplot(Employed) +\n labs(title = \"Total employed\", y = \"Thousands\")\n```\n\n::: {.cell-output-display}\n![](slides_files/figure-beamer/abs3-1.pdf){fig-pos='H'}\n:::\n:::\n\n## The ABS stuff-up\n\n::: {.cell}\n\n```{.r .cell-code}\nemployed |>\n filter(Year >= 2005) |>\n autoplot(Employed) +\n labs(title = \"Total employed\", y = \"Thousands\")\n```\n\n::: {.cell-output-display}\n![](slides_files/figure-beamer/abs4-1.pdf){fig-pos='H'}\n:::\n:::\n\n## The ABS stuff-up\n\n::: {.cell}\n\n```{.r .cell-code}\nemployed |>\n filter(Year >= 2005) |>\n gg_season(Employed, labels = \"right\") +\n labs(title = \"Total employed\", y = \"Thousands\")\n```\n\n::: {.cell-output-display}\n![](slides_files/figure-beamer/abs5-1.pdf){fig-pos='H'}\n:::\n:::\n\n## The ABS stuff-up\n\n::: {.cell}\n\n```{.r .cell-code}\nemployed |>\n mutate(diff = difference(Employed)) |>\n filter(Month == \"Sep\") |>\n ggplot(aes(y = diff, x = 1)) +\n geom_boxplot() +\n coord_flip() +\n labs(title = \"Sep - Aug: total employed\", y = \"Thousands\") +\n scale_x_continuous(breaks = NULL, labels = NULL)\n```\n\n::: {.cell-output-display}\n![](slides_files/figure-beamer/abs6-1.pdf){fig-pos='H'}\n:::\n:::\n\n## The ABS stuff-up\n\n::: {.cell}\n\n```{.r .cell-code}\ndcmp <- employed |>\n filter(Year >= 2005) |>\n model(stl = STL(Employed ~ season(window = 11), robust = TRUE))\ncomponents(dcmp) |> autoplot()\n```\n\n::: {.cell-output-display}\n![](slides_files/figure-beamer/abs7-1.pdf){fig-pos='H'}\n:::\n:::\n\n## The ABS stuff-up\n\n::: {.cell}\n\n```{.r .cell-code}\ncomponents(dcmp) |>\n filter(year(Time) == 2013) |>\n gg_season(season_year) +\n labs(title = \"Seasonal component\") + guides(colour = \"none\")\n```\n\n::: {.cell-output-display}\n![](slides_files/figure-beamer/abs8-1.pdf){fig-pos='H'}\n:::\n:::\n\n## The ABS stuff-up\n\n::: {.cell}\n\n```{.r .cell-code}\ncomponents(dcmp) |>\n as_tsibble() |>\n autoplot(season_adjust)\n```\n\n::: {.cell-output-display}\n![](slides_files/figure-beamer/abs9-1.pdf){fig-pos='H'}\n:::\n:::\n\n## The ABS stuff-up\n\n * August 2014 employment numbers higher than expected.\n * Supplementary survey usually conducted in August for employed people.\n * Most likely, some employed people were claiming to be unemployed in August to avoid supplementary questions.\n * Supplementary survey not run in 2014, so no motivation to lie about employment.\n * In previous 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