ts.lstm {TSLSTM}

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.lstm(
  ts,
  xreg = NULL,
  tsLag,
  xregLag = 0,
  LSTMUnits,
  DropoutRate = 0,
  Epochs = 10,
  CompLoss = "mse",
  CompMetrics = "mae",
  ActivationFn = "tanh",
  SplitRatio = 0.8,
  ValidationSplit = 0.1
)

Arguments

ts

Time series data

xreg

Exogenous variables

tsLag

Lag of time series data

xregLag

Lag of exogenous variables

LSTMUnits

Number of unit in LSTM layer

DropoutRate

Dropout rate

Epochs

Number of epochs

CompLoss

Loss function

CompMetrics

Metrics

ActivationFn

Activation function

SplitRatio

Training and testing data split ratio

ValidationSplit

Validation split ration

Value

• TrainFittedValue: Fitted value of train data

• TestPredictedValue: Predicted value of test data

• AccuracyTable: RMSE and MAPE of train and test data

References

Paul, R.K. and Garai, S. (2021). Performance comparison of wavelets-based machine learning technique for forecasting agricultural commodity prices, Soft Computing, 25(20), 12857-12873

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)
TSLSTM<-ts.lstm(ts=y,xreg = x,tsLag=2,xregLag = 0,LSTMUnits=5, Epochs=2)