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

Creating time series objects in R

Time series plots

Seasonal plots

Trends, seasonality, and cyclicity

Autocorrelation of non-seasonal time series

Autocorrelation of seasonal and cyclic time series

Match the ACF to the time series

White noise

Stock prices and white noise

Exploring and visualizing time series in R

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.

Forecasts and potential futures

Naive forecasting methods

Fitted values and residuals

Checking time series residuals

Training and test sets

Evaluating forecast accuracy of non-seasonal methods

Evaluating forecast accuracy of seasonal methods

Do I have a good forecasting model?

Time series cross-validation

Using tsCV() for time series cross-validation

Benchmark methods and forecast accuracy

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.

Exponentially weighted forecasts

Simple exponential smoothing

SES vs naive

Exponential smoothing methods with trend

Holt's trend methods

Exponential smoothing methods with trend and seasonality

Holt-Winters with monthly data

Holt-Winters method with daily data

State space models for exponential smoothing

Automatic forecasting with exponential smoothing

ETS vs seasonal naive

Match the models to the time series

When does ETS fail?

Exponential smoothing

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.

Transformations for variance stabilization

Box-Cox transformations for time series

Non-seasonal differencing for stationarity

Seasonal differencing for stationarity

ARIMA models

Automatic ARIMA models for non-seasonal time series

Forecasting with ARIMA models

Comparing auto.arima() and ets() on non-seasonal data

Seasonal ARIMA models

Automatic ARIMA models for seasonal time series

Exploring auto.arima() options

Comparing auto.arima() and ets() on seasonal data

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.

Dynamic regression

Forecasting sales allowing for advertising expenditure

Forecasting electricity demand

Dynamic harmonic regression

Forecasting weekly data

Harmonic regression for multiple seasonality

Forecasting call bookings

TBATS models

TBATS models for electricity demand

Your future in forecasting!

Advanced methods

Excelfile in the first exercise

Forecasting involves making predictions about the future. It is required in many situations: deciding whether to build another power generation plant in the next ten years requires forecasts of future demand; scheduling staff in a call centre next week requires forecasts of call volumes; stocking an inventory requires forecasts of stock requirements. Forecasts can be required 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, forecasting is an important aid to effective and efficient planning. This course provides an introduction to time series forecasting using R.

<h2>Use Forecasting in R for Data-Driven Decision Making</h2> 
This course provides an introduction to time series forecasting using R. 
<br><br>
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. 
<br><br>
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. 
<br><br>
<h2>Build Accurate Forecast Models with ARIMA and Exponential Smoothing</h2> 
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. 
<br><br>
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. 
<br><br>
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.

Time Series Analysis in R

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

Harmonic regression for multiple seasonality

“Forecasting in R”

Exercise instructions

Hands-on interactive exercise

Forecasting in R

Chapter 1: Exploring and visualizing time series in R

Chapter 2: Benchmark methods and forecast accuracy

Chapter 3: Exponential smoothing

Chapter 4: Forecasting with ARIMA models

Chapter 5: Advanced methods

What is DataCamp?