The NeuralNet_3L4TS object supports 3-layer regression-like neural networks for Time Series data. Given a response vector y, a predictor matrix x is built that consists of lagged y vectors.
Attributes
- Companion
- class
- Graph
-
- Supertypes
- Self type
-
NeuralNet_3L4TS.type
Members list
Value members
Concrete methods
Create a NeuralNet_3L4TS object from a response vector. The input/data matrix x is formed from the lagged y vectors as columns in matrix x.
Create a NeuralNet_3L4TS object from a response vector. The input/data matrix x is formed from the lagged y vectors as columns in matrix x.
Value parameters
- bakcast
-
whether a backcasted value is prepended to the time series (defaults to false)
- f
-
the activation function family for layers 1->2 (input to hidden)
- f1
-
the activation function family for layers 2->3 (hidden to output)
- fname
-
the feature/variable names
- hh
-
the maximum forecasting horizon (h = 1 to hh)
- hparam
-
the hyper-parameters (use MakeMatrix4TS.hp ++ Optimizer.hp for default)
- itran
-
the inverse transformation function returns response matrix to original scale
- nz
-
the number of nodes in hidden layer (-1 => use default formula)
- tRng
-
the time range, if relevant (time index may suffice)
- xe
-
the matrix of exogenous variable values
- y
-
the original un-expanded output/response vector
Attributes
Create a NeuralNet_3L4TS with automatic rescaling from a data matrix and response matrix.
Create a NeuralNet_3L4TS with automatic rescaling from a data matrix and response matrix.
Value parameters
- bakcast
-
whether a backcasted value is prepended to the time series (defaults to false)
- f
-
the activation function family for layers 1->2 (input to hidden)
- f1
-
the activation function family for layers 2->3 (hidden to output)
- fname
-
the feature/variable names
- hh
-
the maximum forecasting horizon (h = 1 to hh)
- hparam
-
the hyper-parameters
- nz
-
the number of nodes in hidden layer (-1 => use default formula)
- tRng
-
the time range, if relevant (time index may suffice)
- xe
-
the matrix of exogenous variable values
- y
-
the original un-expanded output/response vector