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  3. Forecasting in R
  • 1

    Exploring and visualizing time series in R

    Gratuito

    The first thing to do in any data analysis task is to plot the data. Graphs enable many features of the data to be visualized, including patterns, unusual observations, and changes over time. The features that are seen in plots of the data must then be incorporated, as far as possible, into the forecasting methods to be used.

  • 2

    Benchmark methods and forecast accuracy

    In this chapter, you will learn general tools that are useful for many different forecasting situations. It will describe some methods for benchmark forecasting, methods for checking whether a forecasting method has adequately utilized the available information, and methods for measuring forecast accuracy. Each of the tools discussed in this chapter will be used repeatedly in subsequent chapters as you develop and explore a range of forecasting methods.

  • 3

    Exponential smoothing

    Forecasts produced using exponential smoothing methods are weighted averages of past observations, with the weights decaying exponentially as the observations get older. In other words, the more recent the observation, the higher the associated weight. This framework generates reliable forecasts quickly and for a wide range of time series, which is a great advantage and of major importance to applications in business.

  • 4

    Forecasting with ARIMA models

    ARIMA models provide another approach to time series forecasting. Exponential smoothing and ARIMA models are the two most widely-used approaches to time series forecasting, and provide complementary approaches to the problem. While exponential smoothing models are based on a description of the trend and seasonality in the data, ARIMA models aim to describe the autocorrelations in the data.

  • 5

    Advanced methods

    The time series models in the previous chapters work well for many time series, but they are often not good for weekly or hourly data, and they do not allow for the inclusion of other information such as the effects of holidays, competitor activity, changes in the law, etc. In this chapter, you will look at some methods that handle more complicated seasonality, and you consider how to extend ARIMA models in order to allow other information to be included in the them.

Initializing

Ejercicio

Time series plots

The first step in any data analysis task is to plot the data. Graphs enable you to visualize many features of the data, including patterns, unusual observations, changes over time, and relationships between variables. Just as the type of data determines which forecasting method to use, it also determines which graphs are appropriate.

You can use the autoplot() function to produce a time plot of the data with or without facets, or panels that display different subsets of data:

> autoplot(usnim_2002, facets = FALSE)

The above method is one of the many taught in this course that accepts boolean arguments. Both T and TRUE mean "true", and F and FALSE mean "false", however, T and F can be overwritten in your code. Therefore, you should only rely on TRUE and FALSE to set your indicators for the remainder of the course.

You will use two more functions in this exercise, which.max() and frequency().
which.max() can be used to identify the smallest index of the maximum value

> x <- c(4, 5, 5)
> which.max(x)
[1] 2

To find the number of observations per unit time, use frequency(). Recall the usnim_2002 data from the previous exercise:

> frequency(usnim_2002)
[1] 4

Because this course involves the use of the forecast and ggplot2 packages, they have been loaded into your workspace for you, as well as myts from the previous exercise and the following three series (available in the package forecast):

  • gold containing gold prices in US dollars
  • woolyrnq containing information on the production of woollen yarn in Australia
  • gas containing Australian gas production

Instrucciones

100 XP
  • Plot the data you stored as myts using autoplot() with facetting.
  • Plot the same data without facetting by setting the appropriate argument to FALSE. What happens?
  • Plot the gold, woolyrnq, and gas time series in separate plots.
  • Use which.max() to spot the outlier in the gold series. Which observation was it?
  • Apply the frequency() function to each commodity to get the number of observations per unit time. This would return 52 for weekly data, for example.