Type: | Package |
Title: | Test Bench for the Comparison of Forecast Methods |
Version: | 1.0.1 |
Maintainer: | Neeraj Dhanraj Bokde <neerajdhanraj@gmail.com> |
Description: | Provides a test bench for the comparison of forecasting methods in uni-variate time series. Forecasting methods are compared using different error metrics. Proposed forecasting methods and alternative error metrics can be used. Detailed discussion is provided in the vignette. |
License: | CC0 |
Imports: | PSF, decomposedPSF, ggplot2, gridExtra, imputeTestbench, methods, reshape2, forecast, circlize, RColorBrewer, stats, graphics, utils |
Encoding: | UTF-8 |
LazyData: | true |
RoxygenNote: | 7.0.2 |
Suggests: | knitr, testthat (≥ 2.1.0) |
VignetteBuilder: | knitr |
NeedsCompilation: | no |
Packaged: | 2020-03-06 13:13:25 UTC; au644200 |
Author: | Neeraj Dhanraj Bokde
|
Repository: | CRAN |
Date/Publication: | 2020-03-14 15:40:05 UTC |
Function to append new methods in the study
Description
Function to append new methods in the study
Usage
append_(object, Method, MethodName, ePara, ePara_name)
Arguments
object |
as output of 'prediction_errors()' function |
Method |
as the list of locations of function for the proposed prediction method |
MethodName |
as list of names for function for the proposed prediction method in order |
ePara |
as type of error calculation (RMSE and MAE are default), add an error parameter of your choice in the following manner: ePara = c("errorparametername") where errorparametername is should be a source/function which returns desired error set |
ePara_name |
as list of names of error parameters passed in order |
Value
Returns error comparison for additional forecasting methods
Examples
## Not run:
library(forecast)
test3 <- function(data, nval){return(as.numeric(forecast(ets(data), h = nval)$mean))}
a <- prediction_errors(data = nottem)
b <- append_(object = a, Method = c("test3(data,nval)"), MethodName = c('ETS'))
choose_(object = a)
## End(Not run)
Function to select the desired methods in the study
Description
Function to select the desired methods in the study
Usage
choose_(object)
Arguments
object |
as output of 'prediction_errors()' function |
Value
Returns error comparison for selected forecasting methods
Examples
## Not run:
a <- prediction_errors(data = nottem)
choose_(object = a)
## End(Not run)
Function to use Monte Carlo strategy
Description
Function to use Monte Carlo strategy
Usage
monte_carlo(object, size, iteration, fval = 0, figs = 0)
Arguments
object |
as output of 'prediction_errors()' function |
size |
as volume of time series used in Monte Carlo strategy |
iteration |
as number of iterations models to be applied |
fval |
as a flag to view forecasted values in each iteration (default: 0, don't view values) |
figs |
as a flag to view plots for each iteration (default: 0, don't view plots) |
Value
Error values with provided models in each iteration along with the mean values
Examples
## Not run:
library(forecast)
test3 <- function(data, nval){return(as.numeric(forecast(ets(data), h = nval)$mean))}
a <- prediction_errors(data = nottem,
Method = c("test3(data, nval)"),
MethodName = c("ETS"), append_ = 1)
monte_carlo(object = a1, size = 144, iteration = 10)
## End(Not run)
Function to plot comparison of Prediction methods
Description
Function to plot comparison of Prediction methods
Usage
## S3 method for class 'prediction_errors'
plot(x, ...)
Arguments
x |
as output object of 'prediction_errors()' function |
... |
arguments passed to or from other methods |
Value
Returns error comparison plots for forecasting methods
Examples
a <- prediction_errors(data = nottem)
b <- plot(a)
Function to plot comparison of Predicted values in a circular ring
Description
Function to plot comparison of Predicted values in a circular ring
Usage
plot_circle(x, ...)
Arguments
x |
as output object of 'prediction_errors()' function |
... |
arguments passed to or from other methods |
Value
Returns error comparison plots for forecasting methods
Examples
a <- prediction_errors(data = nottem)
plot_circle(a)
Function working as testbench for comparison of Prediction methods
Description
Function working as testbench for comparison of Prediction methods
Usage
prediction_errors(
data,
nval,
ePara,
ePara_name,
Method,
MethodName,
strats,
dval,
append_
)
Arguments
data |
as input time series for testing |
nval |
as an integer to decide number of values to predict |
ePara |
as type of error calculation (RMSE and MAE are default), add an error parameter of your choice in the following manner: ePara = c("errorparametername") where errorparametername is should be a source/function which returns desired error set |
ePara_name |
as list of names of error parameters passed in order |
Method |
as the list of locations of function for the proposed prediction method (should be recursive) (default:arima) |
MethodName |
as list of names for function for the proposed prediction method in order |
strats |
as list of forecasting strategies. Available : recursive and dirrec |
dval |
as last d values of the data to be used for forecasting |
append_ |
suggests if the function is used to append to another instance |
Value
Returns error comparison for forecasting methods
Examples
prediction_errors(data = nottem)