iForecast (version 1.0.4)

ttsAutoML: Train time series by automatic machine learning of h2o provided by H2O.ai

Description

It generates both the static and recursive time series plots of H2O.ai object generated by package h2o provided by H2O.ai.

Usage

ttsAutoML(y,x=NULL,train.end,arOrder=2,xregOrder=0,maxSecs=30)

Arguments

y

The time series vector of target variable, or the dependent variable, with zoo format, must have dimension. y can be either binary or continuous.

x

The time series matrix of input variables, or the independent variables, with zoo format.

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=1:5; or discontinuous lags like xregOrder=c(0,3,5); zero is allowed since contemporaneous correlation is allowed.

maxSecs

The maximal run time specified, in seconds. Default=20.

Value

output

Output object generated by train function of caret.

arOrder

The autoregressive order of the target variable used.

data

The dataset of imputed.

dataused

The data used by arOrder, xregOrder

Details

This function calls the h2o.automl function from package h2o to execute automatic machine learning estimation. When execution finished, it computes two types of time series forecasts: static and recursive. The procedure of h2o.automl automatically generates a lot of time features.

Examples

# NOT RUN {
# Cross-validation takes time, example below is commented.
data("macrodata")
dep<-macrodata[,"unrate",drop=FALSE]
ind<-macrodata[,-1,drop=FALSE]

# Choosing the dates of training and testing data
train.end<-"2008-12-01"

#autoML of H2O.ai

#autoML <- ttsAutoML(y=dep, x=ind, train.end,arOrder=c(2,4),
# xregOrder=c(0,1,3), maxSecs =30)
#testData2 <- window(autoML$dataused,start="2009-01-01",end=end(autoML$data))
#P1<-iForecast(Model=autoML,newdata=testData2,type="staticfit")
#P2<-iForecast(Model=autoML,newdata=testData2,type="recursive")

#tail(cbind(testData2[,1],P1))
#tail(cbind(testData2[,1],P2))


# }