Autocorrelazione di serie temporali non stagionali

Un altro modo per osservare i dati di una serie temporale è tracciare ciascuna osservazione rispetto a un’altra avvenuta in precedenza usando gglagplot(). Per esempio, puoi tracciare \(y_t\) rispetto a \(y_{t-1}\). Questo si chiama lag plot perché metti a confronto la serie temporale con i propri ritardi.

Le correlazioni associate ai lag plot costituiscono quella che viene chiamata funzione di autocorrelazione (ACF). La funzione ggAcf() produce i grafici dell’ACF.

In questo esercizio lavorerai con i dati oil già caricati (disponibili nel pacchetto fpp2), che contengono la produzione annuale di petrolio in Arabia Saudita dal 1965 al 2013 (misurata in milioni di tonnellate).

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Istruzioni dell'esercizio

Usa la funzione autoplot() per tracciare i dati oil.
Per i dati oil, rappresenta la relazione tra \(y_t\) e \(y_{t-k}\), \(k=1,\dots,9\) usando una delle due funzioni introdotte sopra. Osserva come cambiano le relazioni al crescere del ritardo.
Allo stesso modo, traccia le correlazioni associate a ciascun lag plot usando l’altra funzione nuova appropriata.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create an autoplot of the oil data
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# Create a lag plot of the oil data
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# Create an ACF plot of the oil data
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Questo esercizio fa parte del corso

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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: Benvenuto in Forecasting in R Exercise 2: Creare oggetti di serie temporali in R Exercise 3: Grafici di serie temporali Exercise 4: Grafici stagionali Exercise 5: Trend, stagionalità e ciclicità Exercise 6: Autocorrelazione di serie temporali non stagionali

Esercizio in corso

Exercise 7: Autocorrelazione di serie temporali stagionali e cicliche Exercise 8: Abbina l'ACF alla serie temporale Exercise 9: Rumore bianco Exercise 10: Prezzi azionari e rumore bianco

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: 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!