Forecasting in R

Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.

5 Hours18 Videos55 Exercises43,574 Learners4450 XPQuantitative Analyst TrackTime Series Track

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Use Forecasting in R for Data-Driven Decision Making

This course provides an introduction to time series forecasting using R.

Forecasting involves making predictions about the future. It is required in many situations, such as deciding whether to build another power generation plant in the next ten years or scheduling staff in a call center next week.

Forecasts may be needed several years in advance (for the case of capital investments), or only a few minutes beforehand (for telecommunication routing). Whatever the circumstances or time horizons involved, reliable forecasting is essential to good data-driven decision-making.

Build Accurate Forecast Models with ARIMA and Exponential Smoothing

You’ll start this course by creating time series objects in R to plot your data and discover trends, seasonality, and repeated cycles. You’ll be introduced to the concept of white noise and look at how you can conduct a Ljung-Box test to confirm randomness before moving on to the next chapter, which details benchmarking methods and forecast accuracy.

Being able to test and measure your forecast accuracy is essential for developing usable models. This course reviews a variety of methods before diving into exponential smoothing and ARIMA models, which are two of the most widely-used approaches to time series forecasting.

Before you complete the course, you’ll learn how to use advanced ARIMA models to include additional information in them, such as holidays and competitor activity.
1. 1

Exploring and visualizing time series in R

Free

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.

Welcome to Forecasting Using R
50 xp
Creating time series objects in R
100 xp
Time series plots
100 xp
Seasonal plots
100 xp
Trends, seasonality, and cyclicity
50 xp
Autocorrelation of non-seasonal time series
100 xp
Autocorrelation of seasonal and cyclic time series
100 xp
Match the ACF to the time series
50 xp
White noise
50 xp
Stock prices and white noise
100 xp
2. 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. 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. 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. 5

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.

In the following tracks

Quantitative AnalystTime Series

Collaborators

Rob J. Hyndman

Professor of Statistics at Monash University

Rob J. Hyndman is Professor of Statistics at Monash University, Australia, and Editor-in-Chief of the International Journal of Forecasting. Rob is the author of over 150 research papers and books in statistical science. In 2007, he received the Moran medal from the Australian Academy of Science for his contributions to statistical research, especially in the area of statistical forecasting. He is the author of about 20 R packages, including the popular forecast package.

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