R: Time series prediction models

ARIMA {TSPred}

R Documentation

Time series prediction models

Description

Constructors for the modeling class representing a time series modeling and prediction method based on a particular model.

Usage

ARIMA(train_par = list(), pred_par = list(level = c(80, 95)))

ETS(train_par = list(), pred_par = list(level = c(80, 95)))

HW(train_par = list(), pred_par = list(level = c(80, 95)))

TF(train_par = list(), pred_par = list(level = c(80, 95)))

NNET(
  size = 5,
  train_par = NULL,
  pred_par = list(level = c(80, 95)),
  sw = SW(window_len = size + 1),
  proc = list(MM = MinMax())
)

RFrst(
  ntree = 500,
  train_par = NULL,
  pred_par = list(level = c(80, 95)),
  sw = SW(window_len = 6),
  proc = list(MM = MinMax())
)

RBF(
  size = 5,
  train_par = NULL,
  pred_par = list(level = c(80, 95)),
  sw = SW(window_len = size + 1),
  proc = list(MM = MinMax())
)

SVM(
  train_par = NULL,
  pred_par = list(level = c(80, 95)),
  sw = SW(window_len = 6),
  proc = list(MM = MinMax())
)

MLP(
  size = 5,
  train_par = NULL,
  pred_par = list(level = c(80, 95)),
  sw = SW(window_len = size + 1),
  proc = list(MM = MinMax())
)

ELM(
  train_par = list(),
  pred_par = list(),
  sw = SW(window_len = 6),
  proc = list(MM = MinMax())
)

Tensor_CNN(
  train_par = NULL,
  pred_par = list(level = c(80, 95)),
  sw = SW(window_len = 6),
  proc = list(MM = MinMax())
)

Tensor_LSTM(
  train_par = NULL,
  pred_par = list(batch_size = 1, level = c(80, 95)),
  sw = SW(window_len = 6),
  proc = list(MM = MinMax())
)

Arguments

`train_par`	List of named parameters required by `train_func`.
`pred_par`	List of named parameters required by `pred_func`.
`size`	See `mlp`
`sw`	A `SW` object regarding sliding windows processing.
`proc`	A list of `processing` objects regarding any pre(post)processing needed during training or prediction.
`ntree`	See `randomForest`

Value

An object of class modeling.

Linear models

ARIMA: ARIMA model. train_func set as auto.arima and pred_func set as forecast.

ETS: Exponential Smoothing State Space model. train_func set as ets and pred_func set as forecast.

HW: Holt-Winter's Exponential Smoothing model. train_func set as hw and pred_func set as forecast.

TF: Theta Forecasting model. train_func set as thetaf and pred_func set as forecast.

Machine learning models

NNET: Artificial Neural Network model. train_func set as nnet and pred_func set as predict.

RFrst: Random Forest model. train_func set as randomForest and pred_func set as predict.

RBF: Radial Basis Function (RBF) Network model. train_func set as rbf and pred_func set as predict.

SVM: Support Vector Machine model. train_func set as tune.svm and pred_func set as predict.

MLP: Multi-Layer Perceptron (MLP) Network model. train_func set as mlp and pred_func set as predict.

ELM: Extreme Learning Machine (ELM) model. train_func set as elm_train and pred_func set as elm_predict.

Tensor_CNN: Convolutional Neural Network - TensorFlow. train_func based on functions from tensorflow and keras, and pred_func set as predict.

Tensor_LSTM: Long Short Term Memory Neural Networks - TensorFlow. train_func based on functions from tensorflow and keras, and pred_func set as predict.

Author(s)

Rebecca Pontes Salles