onlineforecast (version 1.0.2)

lm_predict: Prediction with an lm forecast model.

Description

Use a fitted forecast model to predict its output variable with transformed data.

Usage

lm_predict(model, datatr)

Value

The Yhat forecast matrix with a forecast for each model$kseq and for each time point in datatr$t.

Arguments

model

Onlineforecast model object which has been fitted.

datatr

Transformed data.

Details

See the ??ref(recursive updating vignette, not yet available).

Examples

# Take data
D <- subset(Dbuilding, c("2010-12-15", "2011-01-01"))
D$y <- D$heatload
# Define a model 
model <- forecastmodel$new()
model$add_inputs(Ta = "lp(Ta, a1=0.7)", mu = "one()")

# 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

# Transform using the mdoel
datatr <- model$transform_data(D)

# See the transformed data
str(datatr)

# The model has not been fitted
model$Lfits

# To fit
lm_fit(model=model, data=D)

# Now the fits for each horizon are there (the latest update)
# For example 
summary(model$Lfits$k1)

# Use the fit for prediction
D$Yhat <- lm_predict(model, datatr)

# Plot it
plot_ts(D, c("y|Yhat"), kseq=1)