Package 'TSLSTMplus'

LSTMModel class

Description

LSTMModel class for further use in predict function

Usage

LSTMModel(
  lstm_model,
  scale_output,
  scaler_output,
  scale_input,
  scaler_input,
  tsLag,
  xregLag,
  model_structure,
  batch_size,
  lags_as_sequences,
  stateful
)

Arguments

lstm_model	LSTM 'keras' model
scale_output	indicate which type of scaler is used in the output
scaler_output	Scaler of output variable (and lags)
scale_input	indicate which type of scaler is used in the input(s)
scaler_input	Scaler of input variable(s) (and lags)
tsLag	Lag of time series data
xregLag	Lag of exogenous variables
model_structure	Summary of the LSTM model previous to training
batch_size	Batch size used during training of the model
lags_as_sequences	Flag to indicate the model has been trained statefully
stateful	Flag to indicate if LSTM layers shall retain its state between batches.

Value

LSTMModel object

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

if (keras::is_keras_available()){
  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,
                  ScaleInput = 'scale',
                  ScaleOutput = 'scale',
                  Epochs=2)
}

---

Min-Max Scaling of a Matrix

Description

This function applies min-max scaling to a matrix. Each column of the matrix is scaled independently. The scaling process transforms the values in each column to a specified range, typically [0, 1]. The function subtracts the minimum value of each column (if 'min' is 'TRUE' or a numeric vector) and then divides by the range of each column (if 'range' is 'TRUE' or a numeric vector).

Usage

minmax_scale(x, min = TRUE, range = TRUE)

Arguments

x	A numeric matrix whose columns are to be scaled.
min	Logical or numeric vector. If 'TRUE', the minimum value of each column is subtracted. If a numeric vector is provided, it must have a length equal to the number of columns in 'x', and these values are subtracted from each corresponding column.
range	Logical or numeric vector. If 'TRUE', each column is divided by its range. If a numeric vector is provided, it must have a length equal to the number of columns in 'x', and each column is divided by the corresponding value in this vector.

Value

A matrix with the same dimensions as 'x', where each column has been scaled according to the min-max scaling process.

Examples

data <- matrix(rnorm(100), ncol = 10)
  scaled_data <- minmax_scale(data)

---

Predict using a Trained LSTM Model

Description

This function makes predictions using a trained LSTM model for time series forecasting. It performs iterative predictions where each step uses the prediction from the previous step. The function takes into account the lags in both the time series data and the exogenous variables.

Usage

## S3 method for class 'LSTMModel'
predict(
  object,
  ts,
  xreg = NULL,
  xreg.new = NULL,
  horizon = NULL,
  BatchSize = NULL,
  ...
)

Arguments

object	An LSTMModel object containing a trained LSTM model along with normalization parameters and lag values.
ts	A vector or time series object containing the historical time series data. It should have a number of observations at least equal to the lag of the time series data.
xreg	(Optional) A matrix or data frame of exogenous variables to be used for prediction. It should have a number of rows at least equal to the lag of the exogenous variables.
xreg.new	(Optional) A matrix or data frame of exogenous variables to be used for prediction. It should have a number of rows at least equal to the lag of the exogenous variables.
horizon	The number of future time steps to predict.
BatchSize	(Optional) Batch size to use during prediction
...	Optional arguments, no use is contemplated right now

Value

A vector containing the forecasted values for the specified horizon.

Examples

if (keras::is_keras_available()){
      y<-rnorm(100,mean=100,sd=50)
      x1<-rnorm(150,mean=50,sd=50)
      x2<-rnorm(150, mean=50, sd=25)
      x<-cbind(x1,x2)
      x.tr <- x[1:100,]
      x.ts <- x[101:150,]
      TSLSTM<-ts.lstm(ts=y,
                      xreg = x.tr,
                      tsLag=2,
                      xregLag = 0,
                      LSTMUnits=5,
                      ScaleInput = 'scale',
                      ScaleOutput = 'scale',
                      Epochs=2)
      current_values <- predict(TSLSTM, xreg = x.tr, ts = y)
      future_values <- predict(TSLSTM, horizon=50, xreg = x, ts = y, xreg.new = x.ts)
   }

---

Summary of a Trained LSTM Model

Description

This function generates the summary of the LSTM model.

Usage

## S3 method for class 'LSTMModel'
summary(object, ...)

Arguments

object	An LSTMModel object containing a trained LSTM model along with normalization parameters and lag values.
...	Optional arguments, no use is contemplated right now

Value

A vector containing the forecasted values for the specified horizon.

Examples

if (keras::is_keras_available()){
      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,
                      ScaleInput = 'scale',
                      ScaleOutput = 'scale',
                      Epochs=2)
      # Assuming TSLSTM is an LSTMModel object created using ts.lstm function
      summary(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 = NULL,
  xregLag = 0,
  LSTMUnits,
  DenseUnits = NULL,
  DropoutRate = 0,
  Epochs = 10,
  CompLoss = "mse",
  CompMetrics = "mae",
  Optimizer = optimizer_rmsprop,
  ScaleOutput = c(NULL, "scale", "minmax"),
  ScaleInput = c(NULL, "scale", "minmax"),
  BatchSize = 1,
  LSTMActivationFn = "tanh",
  LSTMRecurrentActivationFn = "sigmoid",
  DenseActivationFn = "relu",
  ValidationSplit = 0.1,
  verbose = 2,
  RandomState = NULL,
  EarlyStopping = callback_early_stopping(monitor = "val_loss", min_delta = 0, patience =
    3, verbose = 0, mode = "auto"),
  LagsAsSequences = TRUE,
  Stateful = FALSE,
  ...
)

Arguments

ts	Time series data
xreg	Exogenous variables
tsLag	Lag of time series data. If NULL, no lags of the output are used.
xregLag	Lag of exogenous variables
LSTMUnits	Number of unit in LSTM layers
DenseUnits	Number of unit in Extra Dense layers. A Dense layer with a single neuron is always added at the end.
DropoutRate	Dropout rate
Epochs	Number of epochs
CompLoss	Loss function
CompMetrics	Metrics
Optimizer	'keras' optimizer
ScaleOutput	Flag to indicate if ts shall be scaled before training
ScaleInput	Flag to indicate if xreg shall be scaled before training
BatchSize	Batch size to use during training
LSTMActivationFn	Activation function for LSTM layers
LSTMRecurrentActivationFn	Recurrent activation function for LSTM layers
DenseActivationFn	Activation function for Extra Dense layers
ValidationSplit	Validation split ration
verbose	Indicate how much information is given during training. Accepted values, 0, 1 or 2.
RandomState	seed for replication
EarlyStopping	EarlyStopping according to 'keras'
LagsAsSequences	Use lags as previous timesteps of features, otherwise use them as "extra" features.
Stateful	Flag to indicate if LSTM layers shall retain its state between batches.
...	Extra arguments passed to keras::layer_lstm

Value

LSTMmodel object

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

if (keras::is_keras_available()){
      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,
                      ScaleInput = 'scale',
                      ScaleOutput = 'scale',
                      Epochs=2)
  }

---

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)

---