TSLSTMplus (version 1.0.6)

ts.prepare.data: Prepare data for Long Short Term Memory (LSTM) Model for Time Series Forecasting

Description

The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Min-Max transformation has been used for data preparation. Here, we have used one LSTM layer as a simple LSTM model and a Dense layer is used as the output layer. Then, compile the model using the loss function, optimizer and metrics. This package is based on 'keras' and TensorFlow modules.

Usage

ts.prepare.data(ts, xreg = NULL, tsLag, xregLag = 0)

Value

dataset with all lags created from exogenous and time series data.

Arguments

ts

Time series data

xreg

Exogenous variables

tsLag

Lag of time series data

xregLag

Lag of exogenous variables

Examples

  y <- rnorm(100,mean=100,sd=50)
  x1 <- rnorm(100,mean=50,sd=50)
  x2 <- rnorm(100, mean=50, sd=25)
  x <- cbind(x1,x2)
  ts.prepare.data(y, x, 2, 4)