Holt-Winters-methode met dagelijkse data

De Holt-Winters-methode kan ook worden gebruikt voor dagelijkse data, waarbij het seizoenspatroon lengte 7 heeft en de juiste tijdseenheid voor h dagen zijn.

Hier vergelijk je een additieve Holt-Winters-methode met een seizoensgebonden naive()-methode voor de hyndsight-data. Deze bevatten de dagelijkse pageviews op de Hyndsight-blog gedurende één jaar vanaf 30 april 2014. De data zijn beschikbaar in je workspace.

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Gebruik subset.ts() om een trainingset te maken waarbij de laatste 4 weken van de beschikbare data in hyndsight zijn weggelaten.
Maak voorspellingen voor deze laatste 4 weken met hw() en additieve seizoenscomponent toegepast op de trainingsdata. Ken dit toe aan fchw.
Maak seizoensgebonden naïeve voorspellingen voor dezelfde periode. Gebruik de juiste functie die in een vorige hoofdstuk is geïntroduceerd en ken dit toe aan fcsn.
Welke van de twee voorspellingen is beter op basis van RMSE? Gebruik de functie accuracy() om dit te bepalen.
Maak tijdplots van deze voorspellingen.

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Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Create training data with subset()
train <- subset(___, end = ___)

# Holt-Winters additive forecasts as fchw
fchw <- hw(___, seasonal = ___, h = ___)

# Seasonal naive forecasts as fcsn
fcsn <- ___

# Find better forecasts with accuracy()
accuracy(___, ___)
accuracy(___, ___)

# Plot the better forecasts
autoplot(___)

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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: Exponentieel gewogen voorspellingen Exercise 2: Eenvoudige exponentiële afvlakking Exercise 3: SES vs naive Exercise 4: Exponentiële gladstrijkmethoden met trend Exercise 5: Trendomethoden van Holt Exercise 6: Exponentiële-smoothingmethoden met trend en seizoenspatroon Exercise 7: Holt-Winters met maandelijkse data Exercise 8: Holt-Winters-methode met dagelijkse data

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Exercise 9: Toestandsruimtemodellen voor exponentiële afvlakking Exercise 10: Automatisch voorspellen met exponentiële afvlakking Exercise 11: ETS versus seizoensgebonden naïef Exercise 12: Koppel de modellen aan de tijdreeksen Exercise 13: Wanneer faalt 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!