## ----output.lines=10----------------------------------------------------------------------------------------------
# Load the package and data
library(onlineforecast)
D <- subset(Dbuilding, c("2010-12-15", "2011-01-01"))
D$y <- D$heatload

# Define a simple model 
model <- forecastmodel$new()
model$add_inputs(Ta = "Ta",
                 mu = "one()")
model$add_regprm("rls_prm(lambda=0.99)")
# Period to include in the evaluation of the score function
D$scoreperiod <- in_range("2010-12-20", D$t)
# And the sequence of horizons to fit for
model$kseq <- 1:6
# Add bounds for lambda (lower, init, upper)
model$add_prmbounds(lambda = c(0.9, 0.98, 0.999))


## ----eval=FALSE---------------------------------------------------------------------------------------------------
## # Set debugging of the one() function
## debug(one)
## # Do the transformation, which will stop when entering one()
## model$transform_data(D)
## # Stop the debugging of one()
## undebug(one)


## ----message=FALSE------------------------------------------------------------------------------------------------
# The optimization optimization of lambda
rls_optim(model, D)$par


## ----message=FALSE------------------------------------------------------------------------------------------------
tstart <- Sys.time()
# Caching can be done by providing a path
rls_optim(model, D, cachedir="cache")$par
Sys.time() - tstart


## -----------------------------------------------------------------------------------------------------------------
# See the cache files
dir("cache")


## ----message=FALSE------------------------------------------------------------------------------------------------
tstart <- Sys.time()
# So running again the result is read from the file
rls_optim(model, D, cachedir="cache")$par
Sys.time() - tstart


## ----message=FALSE------------------------------------------------------------------------------------------------
# Change the lower bound for optimization
model$add_prmbounds(lambda = c(0.89, 0.98, 0.999))
# New optimization results are calculated and cached
val <- rls_optim(model, D, cachedir="cache")
dir("cache")


## ----cache=FALSE--------------------------------------------------------------------------------------------------
# To delete the cache folder
unlink("cache", recursive=TRUE)


## ----warning=FALSE------------------------------------------------------------------------------------------------
D <- Dbuilding
# Use plotly
library(plotly)
# usely=TRUE
L <- plot_ts(D, c("heatload","Ta"), kseq=c(1,24), usely=TRUE, xlab="Time",
             ylabs=c("Heat (kW)","Temperature (C)"))
# or exact same
L <- plotly_ts(D, c("heatload","Ta"), kseq=c(1,24), xlab="Time",
               ylabs=c("Heat (kW)","Temperature (C)"))
# 
# From plotly the figures are returned and can be further manipulated
# e.g. put the legend in the top by
L[[length(L)]] <- L[[length(L)]] %>% layout(legend = list(x = 100, y = 0.98))
print(subplot(L, shareX=TRUE, nrows=length(L), titleY = TRUE))

#' \donttest{
#' D <- Dbuilding
#' 
#' plotly_ts(D, c("heatload","Ta"), kseq=c(1,24))
#' plotly_ts(D, c("heatload","Ta$|Taobs$"), kseq=c(1,24))
#' }



## ----warning=FALSE------------------------------------------------------------------------------------------------
D$scoreperiod <- in_range("2010-12-22", D$t)
model <- forecastmodel$new()
model$output = "heatload"
model$add_inputs(Ta = "Ta",
                 mu = "one()")
model$add_regprm("rls_prm(lambda=0.9)")
model$kseq <- c(3,18)
fit1 <- rls_fit(NA, model, D, returnanalysis = TRUE)

# Plot it
plot_ts(fit1)

# Plot it with plotly
plot_ts(fit1, usely=TRUE)