Previsioni con dati settimanali

Con i dati settimanali è difficile gestire la stagionalità usando modelli ETS o ARIMA perché la lunghezza stagionale è troppo grande (circa 52). In alternativa, puoi usare la regressione armonica, che utilizza seni e coseni per modellare la stagionalità.

La funzione fourier() semplifica la generazione delle armoniche necessarie. Più alto è l’ordine (\(K\)), più "ondulato" può essere l’andamento stagionale. Con \(K=1\), è una semplice sinusoide. Puoi scegliere il valore di \(K\) minimizzando l’AICc. Come hai visto nel video, fourier() richiede una serie temporale, il numero di termini di Fourier da generare e, in modo opzionale, il numero di righe da prevedere:

> # fourier(x, K, h = NULL)

> fit <- auto.arima(cafe, xreg = fourier(cafe, K = 6),
                    seasonal = FALSE, lambda = 0)
> fit %>%
    forecast(xreg = fourier(cafe, K = 6, h = 24)) %>%
    autoplot() + ylim(1.6, 5.1)

I dati gasoline pre-caricati comprendono dati settimanali sui prodotti finiti di benzina per motori negli Stati Uniti. In questo esercizio, adatterai una regressione armonica a questo insieme di dati e produrrai previsioni per i prossimi 3 anni.

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Istruzioni dell'esercizio

Crea una matrice xreg chiamata harmonics usando il metodo fourier() su gasoline con ordine \(K=13\), scelto per minimizzare l’AICc.
Stima un modello di regressione dinamica in fit. Imposta xreg uguale a harmonics e seasonal su FALSE perché la stagionalità è gestita dai regressori.
Crea una nuova matrice xreg chiamata newharmonics in modo analogo, quindi calcola le previsioni per i prossimi tre anni come fc.
Infine, traccia il grafico delle previsioni fc.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Set up harmonic regressors of order 13
harmonics <- fourier(___, K = ___)

# Fit regression model with ARIMA errors
fit <- auto.arima(___, xreg = ___, seasonal = ___)

# Forecasts next 3 years
newharmonics <- fourier(___, K = ___, h = ___)
fc <- forecast(___, xreg = ___)

# Plot forecasts fc
___

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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: 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: Regressione dinamica Exercise 2: Prevedere le vendite tenendo conto della spesa pubblicitaria Exercise 3: Prevedere la domanda di elettricità Exercise 4: Regressione armonica dinamica Exercise 5: Previsioni con dati settimanali

Esercizio in corso

Exercise 6: Regressione armonica per stagionalità multiple Exercise 7: Previsioni delle prenotazioni di chiamate Exercise 8: Modelli TBATS Exercise 9: Modelli TBATS per la domanda di elettricità Exercise 10: Il tuo futuro nel forecasting!