Practices (3rd Ed.) textbook provides more detail in how The Evaluating modellingĪccuracy chapter from the Forecasting: Principles and
#Fable 4 plot series#
Model out-performs ARIMA for the series where travellers are on holiday,īusiness, and visiting friends and relatives. It seems that on the training set the ETS To compare how well the models fit the data, we can consider someĬommon accuracy measures. #> 10 Melbourne Victoria Business ets 2000 Q2 499. #> 8 Melbourne Victoria Business ets 1999 Q4 426. innov #> #> 1 Melbourne Victoria Business ets 1998 Q1 405. In particular), the report() function gives a familiar andįit %>% augment() #> # A tsibble: 640 x 9 #> # Key: Region, State, Purpose. If you’re working with a single model (or want to look at one model with 3 more variables: MAE, ar_roots, ma_roots #> 8 Melbourne Victor~ Visiti~ arima 4.24e+3 -442. NA NA #> 7 Melbourne Victor~ Visiti~ ets 1.09e-2 -503. #> 6 Melbourne Victor~ Other arima 4.89e+2 -356.
NA NA #> 5 Melbourne Victor~ Other ets 4.97e+2 -422. #> 4 Melbourne Victor~ Holiday arima 3.07e+3 -429. NA NA #> 3 Melbourne Victor~ Holiday ets 1.10e-2 -487. #> 2 Melbourne Victor~ Busine~ arima 3.67e+3 -435. model sigma2 log_lik AIC AICc BIC MSE AMSE #> #> 1 Melbourne Victor~ Busine~ ets 3.53e+3 -498. Same model class and only if those models share the same response (afterįit %>% glance() #> # A tibble: 8 x 14 #> Region State Purpose. Information criteria (AIC, AICc, BIC) are only comparable between the Residual variance and information criteria. Model, and commonly includes descriptions of the model’s fit such as the The glance() function provides a one-row summary of each with abbreviated variable names 1: std.error, 2: statistic Select() and other verbs to focus on the coefficients fromįit %>% select(Region, State, Purpose, arima) %>% coef() #> # A tibble: 13 x 9 #> Region State Purpose. Similarly, theĪRIMA model varies between time series as it has been automaticallyĮxtract the coefficients from the models. We can see that the all four ETS models have an additive trend, and theĮrror and seasonality have been chosen automatically. Is contained within the cells of each model column. The key variables), and a column for each model specification. Fit % model( ets = ETS(Trips ~ trend( "A")), arima = ARIMA(Trips) ) fit #> # A mable: 4 x 5 #> # Key: Region, State, Purpose #> Region State Purpose ets arima #> #> 1 Melbourne Victoria Business #> 2 Melbourne Victoria Holiday #> 3 Melbourne Victoria Other #> 4 Melbourne Victoria Visiting Ī mable contains a row for each time series (uniquely identified by
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