iForecast (version 1.0.4)

ttsLSTM: Train time series by LSTM of tensorflow provided by kera

Description

It generates both the static and recursive time series plots of deep learning LSTM object generated by package tensorflow provided by kera.

Usage

ttsLSTM(y,x=NULL,train.end,arOrder=1,xregOrder=0,type,memoryLoops=10,shape=NULL,dim3=5)

Arguments

y

The time series vector of target variable, or the dependent variable, with zoo format, must have dimension. y can be only continuous, binary case is not allowed at this version.

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.Default is 1.

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.

type

The additional input 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.

memoryLoops

Length of LSTM learning network loop, to achieve better learning results, this not is suggested to be the same as the length of data row. Default is 10.

shape

The second dmension of LSTM array. If NULL, then it will use the number of columns of complete dataset.

dim3

The third dmension of LSTM array. Default is 5.

Value

output

Output object generated by train function of caret.

batch.size

The batch.size used for LSTM network.

k

The third dimension of arrayin LSTM network.

SHAPE

The shape size of array in LSTM network.

arOrder

he autoregressive order of the target variable used.

data

The dataset of used.

dataused

The data used by arOrder, xregOrder, and type

Details

This function calls the function fit of package tensorflow to execute Long-Short Term Memory (LSTM) estimation. When execution finished, it computes two types of time series forecasts: static and recursive.

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"


#RNN with LSTM network
#LSTM<-ttsLSTM(y=dep, x=ind, train.end,arOrder=c(2,4), xregOrder=c(1,4),
# memoryLoops=5, type=c("none","trend","season","both")[4])

#testData3<-window(LSTM$dataused,start="2009-01-01",end=end(LSTM$data))
#P1<-iForecast(Model=LSTM,newdata=testData3,type="staticfit")
#P2<-iForecast(Model=LSTM,newdata=testData3,type="recursive")

#tail(cbind(testData3[,1],P1,P2))



# }