Time series analysis & forecasting using R
Rob J Hyndman
Time series analysis & forecasting using R
Two-day workshop at ANU. 9-10 November 2022
Room 5.02, Marie Reay Teaching Building, Australian National University, Canberra .
It is becoming increasingly common for organizations to collect huge amounts of data over time, and existing time series analysis tools are not always suitable to handle the scale, frequency and structure of the data collected. In this workshop, we will look at some new packages and methods that have been developed to handle the analysis of large collections of time series.
On day 1, we will look at the tsibble data structure for flexibly managing collections of related time series. We will look at how to do data wrangling, data visualizations and exploratory data analysis. We will explore feature-based methods to explore time series data in high dimensions. A similar feature-based approach can be used to identify anomalous time series within a collection of time series, or to cluster or classify time series. Primary packages for day 1 will be tsibble , lubridate and feasts (along with the tidyverse of course).
Day 2 will be about forecasting. We will look at some classical time series models and how they are automated in the fable package. We will look at creating ensemble forecasts and hybrid forecasts, as well as some new forecasting methods that have performed well in large-scale forecasting competitions. Finally, we will look at forecast reconciliation, allowing millions of time series to be forecast in a relatively short time while accounting for constraints on how the series are related.
How to wrangle time series data with familiar tidy tools.
How to compute time series features and visualize large collections of time series.
How to select a good forecasting algorithm for your time series.
How to ensure forecasts of a large collection of time series are coherent.
Is this course for me?
This course will be appropriate for you if you answer yes to these questions:
Do you already use the tidyverse packages in R such as dplyr, tidyr, tibble and ggplot2?
Do you need to analyse large collections of related time series?
Would you like to learn how to use some tidy tools for time series analysis including visualization, decomposition and forecasting?
People who don’t use R regularly, or don’t know the tidyverse packages, are recommended to do the tutorials at learnr.numbat.space beforehand.
Please bring your own laptop with a recent version of R and RStudio installed. The following code will install the main packages needed for the workshop.
install.packages(c("tidyverse","fpp3", "GGally", "sugrrants"))
Download tourism.xlsx from http://robjhyndman.com/data/tourism.xlsx , and read it into R using read_excel() from the readxl package.
Create a tsibble which is identical to the tourism tsibble from the tsibble package.
Find what combination of Region and Purpose had the maximum number of overnight trips on average.
Create a new tsibble which combines the Purposes and Regions, and just has total trips by State.
Lab Session 2
Create time plots of the following four time series: Bricks from aus_production, Lynx from pelt, Close from gafa_stock, Demand from vic_elec.
Use help() to find out about the data in each series.
For the last plot, modify the axis labels and title.
Lab Session 3
Look at the quarterly tourism data for the Snowy Mountains
snowy <- tourism |> filter(Region == "Snowy Mountains")
Use autoplot(), gg_season() and gg_subseries() to explore the data.
What do you learn?
Produce a calendar plot for the pedestrian data from one location and one year.
Lab Session 4
We have introduced the following functions: gg_lag and ACF. Use these functions to explore the four time series: Bricks from aus_production, Lynx from pelt, Close price of Amazon from gafa_stock, Demand from vic_elec. Can you spot any seasonality, cyclicity and trend? What do you learn about the series?
Lab Session 5
You can compute the daily changes in the Google stock price in 2018 using
dgoog <- gafa_stock |> filter(Symbol == "GOOG", year(Date) >= 2018) |> mutate(trading_day = row_number()) |> update_tsibble(index=trading_day, regular=TRUE) |> mutate(diff = difference(Close))
Does diff look like white noise?
Lab Session 6
Consider the GDP information in global_economy. Plot the GDP per capita for each country over time. Which country has the highest GDP per capita? How has this changed over time?
Lab Session 7
For the following series, find an appropriate Box-Cox transformation in order to stabilise the variance.
United States GDP from global_economy
Slaughter of Victorian “Bulls, bullocks and steers” in aus_livestock
Victorian Electricity Demand from vic_elec.
Gas production from aus_production
Why is a Box-Cox transformation unhelpful for the canadian_gas data?
Lab Session 8
canadian_gas |> STL(Volume ~ season(window=7) + trend(window=11)) |> autoplot()
What happens as you change the values of the two window arguments?
How does the seasonal shape change over time? [Hint: Try plotting the seasonal component using gg_season.]
Can you produce a plausible seasonally adjusted series? [Hint: season_adjust is one of the variables returned by STL.]
Lab Session 9
Use GGally::ggpairs() to look at the relationships between the STL-based features. You might wish to change seasonal_peak_year and seasonal_trough_year to factors.
Which is the peak quarter for holidays in each state?
Lab Session 10
Use a feature-based approach to look for outlying series in PBS.
What is unusual about the series you identify as “outliers”.
Lab Session 11
Produce forecasts using an appropriate benchmark method for household wealth (hh_budget). Plot the results using autoplot().
Produce forecasts using an appropriate benchmark method for Australian takeaway food turnover (aus_retail). Plot the results using autoplot().
Lab Session 12
Compute seasonal naïve forecasts for quarterly Australian beer production from 1992.
Test if the residuals are white noise. What do you conclude?
Lab Session 13
Create a training set for household wealth (hh_budget) by witholding the last four years as a test set.
Fit all the appropriate benchmark methods to the training set and forecast the periods covered by the test set.
Compute the accuracy of your forecasts. Which method does best?
Repeat the exercise using the Australian takeaway food turnover data (aus_retail) with a test set of four years.
Lab Session 14
Try forecasting the Chinese GDP from the global_economy data set using an ETS model.
Experiment with the various options in the ETS() function to see how much the forecasts change with damped trend, or with a Box-Cox transformation. Try to develop an intuition of what each is doing to the forecasts.
[Hint: use h=20 when forecasting, so you can clearly see the differences between the various options when plotting the forecasts.]
Lab Session 15
Find an ETS model for the Gas data from aus_production and forecast the next few years.
Why is multiplicative seasonality necessary here?
Experiment with making the trend damped. Does it improve the forecasts?
Lab Session 16
For the United States GDP data (from global_economy):
Fit a suitable ARIMA model for the logged data.
Produce forecasts of your fitted model. Do the forecasts look reasonable?
Lab Session 17
For the Australian tourism data (from tourism):
Fit a suitable ARIMA model for all data.
Produce forecasts of your fitted models.
Check the forecasts for the “Snowy Mountains” and “Melbourne” regions. Do they look reasonable?
Lab Session 18
Repeat the daily electricity example, but instead of using a quadratic function of temperature, use a piecewise linear function with the “knot” around 20 degrees Celsius (use predictors Temperature & Temp2). How can you optimize the choice of knot?
The data can be created as follows.
vic_elec_daily <- vic_elec |> filter(year(Time) == 2014) |> index_by(Date = date(Time)) |> summarise( Demand = sum(Demand)/1e3, Temperature = max(Temperature), Holiday = any(Holiday) ) |> mutate( Temp2 = I(pmax(Temperature-20,0)), Day_Type = case_when( Holiday ~ "Holiday", wday(Date) %in% 2:6 ~ "Weekday", TRUE ~ "Weekend" ) )
Lab Session 19
Repeat Lab Session 16 but using all available data, and handling the annual seasonality using Fourier terms.
Lab Session 20
Prepare aggregations of the PBS data by Concession, Type, and ATC1.
Use forecast reconciliation with the PBS data, using ETS, ARIMA and SNAIVE models, applied to all but the last 3 years of data.
Which type of model works best?
Does the reconciliation improve the forecast accuracy?
Why doesn’t the reconcililation make any difference to the SNAIVE forecasts?