iForecast (version 1.1.1)

tts.caret: Train time series by caret and produce two types of time series forecasts: static and dynamic

Description

It generates both the static and dynamic time series plots of machine learning prediction object generated by package caret.

Usage

tts.caret(
  y,
  x=NULL,
  method,
  train.end,
  arOrder=2,
  xregOrder=0,
  type,
  tuneLength =10,
  preProcess = NULL,
  resampling="boot",
  Number=NULL,
  Repeat=NULL)

Value

output

Output object generated by train function of caret.

arOrder

The autoregressive order of the target variable used.

training.Pred

All tuned prediction values of training data, using besTunes to extract the best prediction.

dataused

The data used by arOrder, xregOrder, and type.

data

The complete data structure

TD

Time dummies used, inherited from type

train.end

The same as argument in tts.caret

Arguments

y

The time series object of the target variable, for example, timeSeries,xts, or zoo. y can be either binary or continuous. Date format must be "

x

The time series matrix of input variables, timestamp is the same as y, maybe null.

method

The train_model_list of caret. While using this, make sure that the method allows regression. Methods in c("svm","rf","rpart","gamboost","BstLm","bstSm","blackboost") are feasible.

train.end

The end date of training data, must be specificed.The default dates of train.start and test.end are the start and the end of input data; and the test.start is the 1-period next of train.end.

arOrder

The autoregressive order of the target variable, which may be sequentially specifed like arOrder=1:5; or discontinuous lags like arOrder=c(1,3,5); zero is not allowed.

xregOrder

The distributed lag structure of the input variables, which may be sequentially specifed like xregOrder=0:5; or discontinuous lags like xregOrder=c(0,3,5); zero is allowed since contemporaneous correlation is allowed.

type

The time dummies variables. We have four selection:
"none"=no other variables,
"trend"=inclusion of time dummy,
"season"=inclusion of seasonal dummies,
"both"=inclusion of both trend and season. No default.

tuneLength

The same as the length specified in train function of package caret.

preProcess

Whether to pre-process the data, current possibilities are "BoxCox", "YeoJohnson", "expoTrans", "center", "scale", "range", "knnImpute", "bagImpute", "medianImpute", "pca", "ica" and "spatialSign".The default is no pre-processing.

resampling

The method for resampling, as trainControl function list in package caret. The default is "boot" for bootstrapping with 25 replications. Current choices are c("cv","boot","repeatedcv","LOOCV") where "cv" is K-fold CV with a default K=10 or specified by the "Number" below, "LOOCV" denotes the leave-one-out CV

Number

The number of K for K-Fold CV, default (NULL) is 10; for "boot" option, the default number of replications is 25

Repeat

The number for the repeatition for "repeatedcv".

Author

Ho Tsung-wu <[e-mail]>, College of Management, National Taiwan Normal University.

Details

This function calls the train function of package caret to execute estimation. When execution finished, we compute two types of time series forecasts: static and recursive.

Examples

# Cross-validation takes time, example below is commented.
## Machine Learning by library(caret)
library(zoo)
#Case 1. Low frequency
data("macrodata")
dep <- macrodata[569:669,"unrate",drop=FALSE]
ind <- macrodata[569:669,-1,drop=FALSE]
train.end <- "2018-12-01"# Choosing the end dating of train

models <- c("glm","knn","nnet","rpart","rf","svm","enet","gbm","lasso","bridge","nb")[2]
type <- c("none","trend","season","both")[1]
output <- tts.caret(y=dep,x=NULL, arOrder=c(1), xregOrder=c(1),
 method=models, tuneLength =1, train.end, type=type,
 resampling=c("boot","cv","repeatedcv")[1],preProcess = "center")

testData1 <- window(output$dataused,start="2019-01-01",end=end(output$dataused))
P1 <- iForecast(Model=output,Type="static",newdata=testData1)
P2 <- iForecast(Model=output,Type="dynamic",n.ahead=nrow(testData1))

tail(cbind(testData1[,1],P1,P2))

#Case 2. High frequency
#head(ES_15m)
#head(ES_Daily)
#dep <- ES_15m #SP500 15-minute realized absolute variance
#ind <- NULL
#train.end <- as.character(rownames(dep))[as.integer(nrow(dep)*0.9)]

#models<-c("svm","rf","rpart","gamboost","BstLm","bstSm","blackboost")[1]
#type<-c("none","trend","season","both")[1]
# output <- tts.caret(y=dep, x=ind, arOrder=c(3,5), xregOrder=c(0,2,4),
# method=models, tuneLength =10, train.end, type=type,
# resampling=c("boot","cv","repeatedcv")[2],preProcess = "center")
#testData1<-window(output$dataused,start="2009-01-01",end=end(output$dataused))
#P1<-iForecast(Model=output,Type="static",newdata=testData1)
#P2<-iForecast(Model=output,Type="dynamic",n.ahead=nrow(testData1))