Voorspellingsnauwkeurigheid van seizoensmethoden evalueren

Zoals je in het eerste hoofdstuk hebt geleerd, specificeert de functie window() het start- en end-punt van een tijdreeks met de relevante tijdstippen in plaats van indexwaarden. Elk van deze twee argumenten kan worden geformatteerd als een vector zoals c(year, period), die je eerder ook als argument voor ts() hebt gebruikt. Opnieuw verwijst period hier naar quarter.

Hier ga je de kwartaalcijfers van bezoekers in Melbourne (visnights[, "VICMetro"]) gebruiken om drie verschillende trainingssets te maken, waarbij je respectievelijk de laatste 1, 2 en 3 jaar weglaat. Bekijk de vooraf geladen visnights-gegevens in je console voordat je begint; dit helpt je de juiste waarde te bepalen voor het sleutelwoord h (dat het aantal waarden dat je wilt voorspellen aangeeft) in je voorspelmethoden.

Bereken vervolgens voor elke trainingsset het volgende jaar aan data en vergelijk ten slotte de mean absolute percentage error (MAPE) van de voorspellingen met accuracy(). Waarom denk je dat de MAPE zoveel varieert?

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Gebruik window() om drie trainingssets te maken van visnights[,"VICMetro"], waarbij je de laatste 1, 2 en 3 jaar weglaat; noem deze respectievelijk train1, train2 en train3. Stel het end-argument hierop in.
Bereken voor elk van de trainingssets één jaar aan voorspellingen met de methode snaive(). Noem deze respectievelijk fc1, fc2 en fc3.
Volg de structuur van de voorbeeldcode en vergelijk de MAPE van de drie sets voorspellingen met de functie accuracy() met je testset.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Create three training series omitting the last 1, 2, and 3 years
train1 <- window(visnights[, "VICMetro"], end = c(2015, 4))
train2 <- ___
train3 <- ___

# Produce forecasts using snaive()
fc1 <- snaive(___, h = ___)
fc2 <- ___
fc3 <- ___

# Use accuracy() to compare the MAPE of each series
accuracy(fc1, visnights[, "VICMetro"])["Test set", "MAPE"]
___
___

Code bewerken en uitvoeren

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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: Voorspellingen en mogelijke toekomsten Exercise 2: Naieve voorspellingsmethoden Exercise 3: Aangepaste waarden en residuen Exercise 4: Residu’s van tijdreeksen controleren Exercise 5: Trainings- en testsets Exercise 6: Voorspellingsnauwkeurigheid evalueren voor niet-seizoensgebonden methoden Exercise 7: Voorspellingsnauwkeurigheid van seizoensmethoden evalueren

Huidige oefening

Exercise 8: Heb ik een goed voorspelmodel?Exercise 9: Tijdreeks-crossvalidatie Exercise 10: tsCV() gebruiken voor tijdreeks-crossvalidatie

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: Exponentially weighted forecasts Exercise 2: Simple exponential smoothing Exercise 3: SES vs naive Exercise 4: Exponential smoothing methods with trend Exercise 5: Holt's trend methods Exercise 6: Exponential smoothing methods with trend and seasonality Exercise 7: Holt-Winters with monthly data Exercise 8: Holt-Winters method with daily data Exercise 9: State space models for exponential smoothing Exercise 10: Automatic forecasting with exponential smoothing Exercise 11: ETS vs seasonal naive Exercise 12: Match the models to the time series Exercise 13: When does ETS fail?

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!