SES vs naive

In questo esercizio metterai in pratica quanto sai su training e test set, sulla funzione subset() e sulla funzione accuracy(), tutte viste nel Capitolo 2, per confrontare le previsioni SES e naive sui dati marathon.

Hai già fatto qualcosa di molto simile per confrontare le previsioni naive e mean in un esercizio precedente, "Evaluating forecast accuracy of non-seasonal methods".

Ripassiamo il processo:

Per prima cosa, importa e carica i dati. Decidi quanta parte dei dati destinare al training e quanta al testing; i set non devono sovrapporsi.
Crea un sottoinsieme dei dati per ottenere il training set, che userai come argomento nelle funzioni di previsione. Facoltativamente, puoi creare anche un test set da usare dopo.
Calcola le previsioni sul training set usando le funzioni di forecasting che preferisci e imposta h uguale al numero di valori che vuoi prevedere, che corrisponde anche alla lunghezza del test set.
Per visualizzare i risultati, usa la funzione accuracy() con la previsione come primo argomento e i dati originali (o il test set) come secondo.
Scegli una metrica in output, come RMSE o MAE, per valutare le previsioni; un errore più piccolo indica maggiore accuratezza.

I dati marathon sono caricati nel tuo workspace.

Questo esercizio fa parte del corso

Previsioni in R

Visualizza il corso

Istruzioni dell'esercizio

Usando subset(), crea un training set per marathon che includa tutti gli anni tranne gli ultimi 20, che terrai da parte per il testing.
Calcola le previsioni SES e naive di questo training set e salvale rispettivamente in fcses e fcnaive.
Calcola le misure di accuratezza delle due serie di previsioni usando la funzione accuracy() nella tua console.
Assegna a fcbest le previsioni migliori (tra fcses e fcnaive) in base all’RMSE.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create a training set using subset()
train <- ___(___, end = length(marathon) - ___)

# Compute SES and naive forecasts, save to fcses and fcnaive
fcses <- ses(___, h = ___)
fcnaive <- naive(___, h = ___)

# Calculate forecast accuracy measures
accuracy(___, ___)
accuracy(___, ___)

# Save the best forecasts as fcbest
fcbest <- ___

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Previsioni in R

InicianteNível de habilidade

4.9+

Inizia il corso gratis

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: Previsioni a pesi esponenziali Exercise 2: Smussamento esponenziale semplice Exercise 3: SES vs naive

Esercizio in corso

Exercise 4: Metodi di smoothing esponenziale con trend Exercise 5: Metodi di trend di Holt Exercise 6: Metodi di smoothing esponenziale con trend e stagionalità Exercise 7: Holt-Winters con dati mensili Exercise 8: Metodo di Holt-Winters con dati giornalieri Exercise 9: Modelli a spazio degli stati per lo smoothing esponenziale Exercise 10: Previsioni automatiche con lo smoothing esponenziale Exercise 11: ETS vs naive stagionale Exercise 12: Abbina i modelli alle serie temporali Exercise 13: Quando ETS fallisce?

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!