onlineforecast (version 0.9.3)

lm_fit: Fit an onlineforecast model with lm

Description

Fit a linear regression model given a onlineforecast model, seperately for each prediction horizon

Usage

lm_fit(
  prm = NA,
  model,
  data,
  scorefun = NA,
  returnanalysis = TRUE,
  printout = TRUE
)

Arguments

prm

as numeric with the parameters to be used when fitting.

model

object of class forecastmodel with the model to be fitted.

data

as data.list with the data to fit the model on.

scorefun

Optional. If scorefun is given, e.g. rmse, then the value of this is also returned.

returnanalysis

as logical determining if the analysis should be returned. See below.

printout

Defaults to TRUE. Prints the parameters for model.

Value

Depends on:

- If returnanalysis is TRUE a list containing:

* Yhat: data.frame with forecasts for model$kseq horizons.

* model: The forecastmodel object cloned deep, so can be modified without changing the original object.

* data: data.list with the data used, see examples on how to obtain the transformed data.

* Lfitval: a character "Find the fits in model$Lfits", it's a list with the lm fits for each horizon.

* scoreval: data.frame with the scorefun result on each horizon (only scoreperiod is included).

- If returnanalysis is FALSE (and scorefun is given): The sum of the score function on all horizons (specified with model$kseq).

Examples

# NOT RUN {
# Take data
D <- subset(Dbuilding, c("2010-12-15", "2011-01-01"))
D$y <- D$heatload
# Define a simple model 
model <- forecastmodel$new()
model$output <- "y"
model$add_inputs(Ta = "Ta",
                 mu = "one()")
model$add_regprm("rls_prm(lambda=0.99)")

# Before fitting the model, define which points to include in the evaluation of the score function
D$scoreperiod <- in_range("2010-12-20", D$t)
# And the sequence of horizons to fit for
model$kseq <- 1:6

# Now we can fit the model with RLS and get the model validation analysis data
fit <- lm_fit(prm=NA, model=model, data=D)
# What did we get back?
names(fit)
class(fit)
# The one-step forecast
plot(D$y, type="l")
lines(fit$Yhat$k1, col=2)
# Get the residuals
plot(residuals(fit)$h1)
# Score for each horizon
score_fit(fit)

# The lm_fit don't put anything in
fit$Lfitval
# Find the lm fits here
model$Lfits
# See result for k=1 horizon
summary(model$Lfits$k1)
# Some diurnal pattern is present
acf(residuals(fit)$h1, na.action=na.pass, lag.max=96)

# }