The NeuralNet_XL4TS object supports X-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}].
Create an NeuralNet_XL object from a response vector. The input/data matrix x is formed from the lagged y vectors as columns in matrix x. In addition, lagged exogenous variables are added. ARX_D.buildMatrix4TS (xe: MatrixD, y: VectorD, p: Int, q: Int, spec: Int, lwave: Double,
Create an NeuralNet_XL object from a response vector. The input/data matrix x is formed from the lagged y vectors as columns in matrix x. In addition, lagged exogenous variables are added. ARX_D.buildMatrix4TS (xe: MatrixD, y: VectorD, p: Int, q: Int, spec: Int, lwave: Double,
Value parameters
elag1
the minimum exo lag included (inclusive)
elag2
the maximum exo lag included (inclusive)
f
the array of activation function family for layers k->k+1
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)