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

In TSLSTMplus: Long-Short Term Memory for Time-Series Forecasting, Enhanced

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)

Arguments

ts	Time series data
xreg	Exogenous variables
tsLag	Lag of time series data
xregLag	Lag of exogenous variables

Value

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

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)

TSLSTMplus documentation built on April 3, 2025, 5:53 p.m.