ttsLSTM | R Documentation |

`tensorflow`

provided by `kera`

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

provided by `kera`

.

```
ttsLSTM(y,
x=NULL,
train.end,
arOrder=1,
xregOrder=0,
type,
memoryLoops=10,
shape=NULL,
dim3=5,
batch.range=2:7,
batch.size=NULL)
```

`y` |
The time series object of the target variable, or the dependent variable, with |

`x` |
The time series matrix of input variables, or the independent variables, with |

`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: |

`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. |

.

`batch.range` |
The range of search batch.size. The code selects the first that satisfies exact division with the rows of data used |

.

`batch.size` |
The number of batch size for LSTM layer. Default is NULL determined by searching among the batch.range. |

.

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.

`output` |
Output object generated by train function of |

`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 |

Ho Tsung-wu <tsungwu@ntnu.edu.tw>, College of Management, National Taiwan Normal University.

```
# 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],
# batch.range=2:7,batch.size=NULL)
#testData3<-window(LSTM$dataused,start="2009-01-01",end=end(LSTM$data))
#P1<-iForecast(Model=LSTM,newdata=testData3,type="static")
#P2<-iForecast(Model=LSTM,newdata=testData3,type="dynamic")
#tail(cbind(testData3[,1],P1,P2))
```

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