scalation.modeling.forecasting.neuralforecasting.NeuralNet_3L4TS
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. y_t = f2 (b dot f(a dot x)) where x = [y_{t-1}, y_{t-2}, ... y_{t-lags}].
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Supertypes
class Object
trait Matchable
class Any
Self type
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Create a NeuralNet_3L 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_3L object from a response vector. The input/data matrix x is formed from the lagged y vectors as columns in matrix x.
Value parameters
f
the activation function family for layers 1->2 (input to output)
f1
the activation function family for layers 2->3 (hidden to output)
h
the forecasting horizon (1, 2, ... h)
hparam
the hyper-parameters (use Optimizer.hp for default)
lags
the maximum lag included (inclusive)
nz
the number of nodes in hidden layer (-1 => use default formula)
y
the original un-expanded output/response vector
Attributes