ttsAutoML: Train time series by automatic machine learning of 'h2o'...

In iForecast: Machine Learning Time Series Forecasting

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 object of the target variable, or the dependent variable, with timeSeries or zoo format, must have dimension. y can be either binary or continuous. Time format must be "
x	The time series matrix of input variables, or the independent variables, with timeSeries or zoo format. Time format must be "
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.

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.

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

Author(s)

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

Examples

# 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="static")
#P2<-iForecast(Model=autoML,newdata=testData2,type="dynamic")

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

iForecast documentation built on July 10, 2023, 1:59 a.m.