Holts Trendmethoden

Holts lokale Trendmethode ist in der Funktion holt() implementiert:

> holt(y, h = 10, ...)

Hier wendest du sie auf die Zeitreihe austa an, die jährliche Zahlen internationaler Besucher in Australia von 1980–2015 enthält (in Millionen). Die Daten wurden bereits in deinen Workspace geladen.

Diese Übung ist Teil des Kurses

<Kurs>Prognosen mit R</Kurs>

Übungsanweisungen

Erstelle 10-Jahres-Prognosen für austa mit Holts Methode. Setze h entsprechend.
Verwende die Funktion summary(), um die Modellparameter und weitere Informationen anzusehen.
Zeichne deine Prognosen mit der Standardfunktion zum Zeitreihenplotten.
Nutze checkresiduals(), um zu prüfen, ob die Residuen weißem Rauschen ähneln.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Produce 10 year forecasts of austa using holt()
fcholt <- ___

# Look at fitted model using summary()
___

# Plot the forecasts
___

# Check that the residuals look like white noise
___

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

<Kurs>Prognosen mit R</Kurs>

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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.

Exercise 1: Welcome to Forecasting Using R Exercise 2: Creating time series objects in R Exercise 3: Time series plots Exercise 4: Seasonal plots Exercise 5: Trends, seasonality, and cyclicity Exercise 6: Autocorrelation of non-seasonal time series Exercise 7: Autocorrelation of seasonal and cyclic time series Exercise 8: Match the ACF to the time series Exercise 9: White noise Exercise 10: Stock prices and white noise

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.

Exercise 1: Forecasts and potential futures Exercise 2: Naive forecasting methods Exercise 3: Fitted values and residuals Exercise 4: Checking time series residuals Exercise 5: Training and test sets Exercise 6: Evaluating forecast accuracy of non-seasonal methods Exercise 7: Evaluating forecast accuracy of seasonal methods Exercise 8: Do I have a good forecasting model?Exercise 9: Time series cross-validation Exercise 10: Using tsCV() for time series cross-validation

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.

Exercise 1: Exponentiell gewichtete Prognosen Exercise 2: Einfache exponentielle Glättung Exercise 3: SES vs. Naive Exercise 4: Exponentielle Glättungsverfahren mit Trend Exercise 5: Holts Trendmethoden

Aktuelle Übung

Exercise 6: Exponential-Smoothing-Methoden mit Trend und Saisonalität Exercise 7: Holt-Winters mit Monatsdaten Exercise 8: Holt-Winters-Verfahren mit täglichen Daten Exercise 9: Zustandsraummodelle für exponentielle Glättung Exercise 10: Automatische Prognosen mit exponentieller Glättung Exercise 11: ETS vs. saisonal-naiv Exercise 12: Ordne die Modelle den Zeitreihen zu Exercise 13: Wann scheitert ETS?

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.

Exercise 1: Transformations for variance stabilization Exercise 2: Box-Cox transformations for time series Exercise 3: Non-seasonal differencing for stationarity Exercise 4: Seasonal differencing for stationarity Exercise 5: ARIMA models Exercise 6: Automatic ARIMA models for non-seasonal time series Exercise 7: Forecasting with ARIMA models Exercise 8: Comparing auto.arima() and ets() on non-seasonal data Exercise 9: Seasonal ARIMA models Exercise 10: Automatic ARIMA models for seasonal time series Exercise 11: Exploring auto.arima() options Exercise 12: 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.

Exercise 1: Dynamic regression Exercise 2: Forecasting sales allowing for advertising expenditure Exercise 3: Forecasting electricity demand Exercise 4: Dynamic harmonic regression Exercise 5: Forecasting weekly data Exercise 6: Harmonic regression for multiple seasonality Exercise 7: Forecasting call bookings Exercise 8: TBATS models Exercise 9: TBATS models for electricity demand Exercise 10: Your future in forecasting!