Chapter 19 adam() cheat sheet for R

This Chapter summarises the main ways of using adam() in R with the references to the chapters and sections, where specific elements are discussed.

Estimate the best ETS model for the provided data (Section 15.1):

The best ETS with a holdout of 10 observations to test the performance of model:

Build a pure multiplicative ETS with seasonality and an arbitrary seasonal lag of 7 (can be applied to an object of any class, Section 15.1):

Estimate the best ARIMA model for the provided data (assuming seasonal lag of 7, Section 15.2):

Build ARIMA(0,1,1) with drift (Chapter 8 for the general ARIMA and Section 8.1.4 for the one with constant):

Estimate ETS(A,N,N)+ARIMA(1,0,0) model (Sections 8.4 9.4):

Use Generalised Normal distribution for the residuals of ADAM ETS(A,A,N) (Sections 5.5, 6.5 and 11.1):

Select the best distribution for the specific ADAM ETS(A,A,N) (Chapter 15):

Select the most appropriate ETSX model for the provided data (which can be any 2-dimensional object, Chapter 10):

Specify, which explanatory variables to include and in what form (Section 10.1):

Select the set of explanatory variables for ETSX(M,N,N) based on AIC (Section 15.3):

Estimate ETS(A,Ad,N) model using a multistep loss function, GTMSE (Section 11.3):

Estimate ARIMA(1,1,2) using a multistep loss function (Section 11.3) with backcasting of initials:

Select and estimate the most appropriate ETS model on the data with multiple frequencies (Chapter 12):

Select and estimate the triple seasonal ARIMA on the data with multiple frequencies (Chapter 12):

Apply an automatically selected the occurrence part of the model to intermittent data (Section 13.1):

Use the estimated occurrence model in adam() to model intermittent data (Section 13.2):

Or alternatively just use the same model for occurrence and demand sizes part (Chapter 13):

Estimate the scale model for previously estimated ADAM (Chapter 17):

Implant the scale model into the ADAM for future use (e.g. for forecasting):

Produce diagnostics plots to see if the ADAM can be improved any further (Chapter 14):

Extract conventional, standardised and studentised residuals (Chapter 14):

Plot time series decomposition according to ADAM ETS (Section 4.1):

Produce point forecast and prediction interval from ADAM for 10 steps ahead (Chapter 18):

Produce point forecast and prediction interval for ADAM, cumulative over the lead time of 10 (Subsection 18.4.3):

Produce point forecast and empirical prediction interval for upper bound (upper quantile of distribution, Sections 18.3.5 and 18.4.2):

Produce summary of ADAM (Chapter 16):

Reapply ADAM with randomly selected initials and parameters and produce forecasts from each of these models (Section 16.4):

Extract multistep forecast errors from ADAM (Subsection 14.7.1):

Extract covariance matrix of multistep forecast errors from ADAM (Section 11.3):

Extract actual values and fitted from ADAM: