% Generated by roxygen2: do not edit by hand % Please edit documentation in R/AR.R \name{AR} \alias{AR} \title{Auto-Regressive (AR) input} \usage{ AR(lags) } \arguments{ \item{lags}{integer vector: The lags of the AR to include.} } \value{ A list of matrices, one for each lag in lags, each with columns according to model$kseq. } \description{ Generate auto-regressive (AR) inputs in a model } \details{ The AR function can be used in an onlineforecast model formulation. It creates the input matrices for including AR inputs in a model during the transformation stage. It takes the values from the model output in the provided data does the needed lagging. The lags must be given according to the one-step ahead model, e.g.: \code{AR(lags=c(0,1))} will give: \eqn{Y_{t+1|t} = \phi_1 y_{t-0} + \phi_2 y_{t-1} + \epsilon_{t+1}} and: \code{AR(lags=c(0,3,12))} will give: \eqn{Y_{t+1|t} = \phi_1 y_{t-0} + \phi_2 y_{t-3} + \phi_3 y_{t-12} + \epsilon_{t+1}} Note, that For k>1 the coefficients will be fitted individually for each horizon, e.g.: \code{AR(lags=c(0,1))} will be the multi-step AR: \eqn{Y_{t+k|t} = \phi_{1,k} y_{t-0} + \phi_{2,k} y_{t-1} + \epsilon_{t+k|t}} See the details in examples on \url{https://onlineforecasting.org}. } \examples{ # Setup data and a model for the example D <- Dbuilding model <- forecastmodel$new() model$output = "heatload" # Use the AR in the transformation stage model$add_inputs(AR = "AR(c(0,1))") # Regression parameters model$add_regprm("rls_prm(lambda=0.9)") # kseq must be added model$kseq <- 1:4 # In the transformation stage the AR input will be generated # See that it generates two input matrices, simply with the lagged heat load at t for every k model$transform_data(subset(D, 1:10)) # Fit with recursive least squares (no parameters prm in the model) fit <- rls_fit(c(lambda=0.99), model, D, returnanalysis=TRUE) # Plot the result, see "?plot_ts.rls_fit" plot_ts(fit, xlim=c(ct("2010-12-20"),max(D$t))) # Plot for a short period with peaks plot_ts(fit, xlim=c("2011-01-05","2011-01-07")) # For online updating, see ??ref{vignette, not yet available}: # the needed lagged output values are stored in the model for next time new data is available model$yAR # The maximum lag needed is also kept model$maxlagAR }