forecastmodel {onlineforecast}

Class for forecastmodels

Description

R6 class for a forecastmodel

Details

This class holds the variables and functions needed for defining and setting up a forecast model - independent of the fitting scheme. See the vignettes on how to setup and use a model and the website https://onlineforecasting.org for more info.

Holds all the information needed independently of the fitting scheme (e.g. lm_fit or rls_fit), see the fields and functions below.

The fields are separated into: - Fields for setting up the model - Fields used when fitting (e.g. which horizons to fit for is set in kseq

See the fields description below.

Note, it's an R6 class, hence an object variable is a pointer (reference), which means two important points: - In order to make a copy, the function clone_deep() must be used (usually clone(deep=TRUE), but that will end in an infinite loop). - It can be manimulated directly in functions (without return). The code is written such that no external functions manipulate the model object, except for online updating.

For online updating (i.e. receiving new data and updating the fit), then the model definition and the data becomes entangled, since transformation functions like low-pass filtering with lp() requires past values. See the vignette ??(ref to online vignette, not yet available) and note that rls_fit() resets the state, which is also done in all xxx_fit functions (e.g. rls_fit.

Public fields used for setting up the model

- output = NA, character: Name of the output.

- inputs = list(), add them with add_inputs(): List of inputs (which are R6 objects) (note the "cloning of list of reference objects" issue below in deep_clone function)

- regprmexpr = NA: The expression (as character) used for generating the regprm, e.g. "rls_prm()" for RLS.

- regprm = list(): Regression parameters calculated by evaluating the regprmexpr.

- prmbounds = as.matrix(data.frame(lower=NA, init=NA, upper=NA)): The bounds for optimization of the parameters, e.g. with rls_optim().

- outputrange = NA, numeric vector of length 2: Limits of the predictions cropped in the range, e.g. outputrange = c(0,Inf) removes all negative output predictions.

Public fields used when the model is fitted

- kseq = NA: The horizons to fit for.

- kseqopt = NA: The horizons to fit for when optimizing.

- p = NA: The (transformation stage) parameters used for the fit.

- Lfits = list(): The regression fits, one for each k in kseq (simply a list with the latest fit).

- datatr = NA: Transformed input data (data.list with all inputs for regression)

Public methods

All public functions are described below and in examples a section for each is included:

$new()

Create a new 'forecastmodel' object.

Returns a forecastmodel object.

$add_inputs(...)

Add inputs to the model.

- ...: The inputs are given as arguments, see examples.

$add_regprm(regprm_expr)

Add expression (as character) which generates regression parameters.

$add_prmbounds(...)

Add the transformation parameters and bounds for optimization.

$get_prmbounds(...)

Get the transformation parameter bounds, used by optimization functions e.g. rls_optim().

$insert_prm(prm)

Insert the transformation parameters prm in the input expressions and regression expressions, and keep them in $prm (simply string manipulation).

$transform_data(data)

Function for transforming the input data to the regression stage input data (see vignette("setup-data", package = "onlineforecast")).

$reset_state()

Resets the input states and stored data for iterative fitting (datatr rows and yAR) (see ??(ref to online updating vignette, not yet available).

$check(data = NA)

Check if the model is setup correctly.

Examples

# New object
model <- forecastmodel$new()

# Print it
model


# Add model inputs
model$add_inputs(Ta = "lp(Ta)")
# See it
model$inputs
# Update to use no low-pass filter
model$add_inputs(Ta = "Ta")
model$inputs
# Add another
model$add_inputs(I = "lp(I)")
model$inputs

# Simply a list, so manipulate directly
class(model$inputs$Ta)
model$inputs$Ta$expr <- "lp(Ta, a1=0.9)"

# Add the parameters for the regression stage
model$add_regprm("rls_prm(lambda=0.99)")
# The evaluation is a list, which is set in
model$regprm


# Set the lambda to be optimized between 0.9 and 0.999, starting at 0.99
model$add_prmbounds(lambda = c(0.9, 0.99, 0.999))
# Note the "__" syntax to set parameters for inputs: "input__prm"
model$add_prmbounds(Ta__a1 = c(0.8, 0.95, 0.99))

# Get the lower bounds
model$get_prmbounds("lower")

# Insert the init parameters
prm <- model$get_prmbounds("init")
prm
# Before
model$inputs$Ta$expr
# After
model$insert_prm(prm)
model$inputs$Ta$expr

# Check if the model is setup and can be used with a given data.list
# An error is thrown
try(model$check(Dbuilding))
# Add the model output
model$output <- "heatload"
# Still not error free
try(model$check(Dbuilding))
# Add the horizons to fit for
model$kseq <- 1:4
# Finally, no errors :)
model$check(Dbuilding)