States Modifications

Padding and Estension

ReservoirComputing.ExtendedStatesType
ExtendedStates()

The states are extended with the input data, for the training section, and the prediction data, during the prediction section. This is obtained with a vertical concatenation of the data and the states.

ReservoirComputing.PaddedStatesType
PaddedStates(padding)
PaddedStates(;padding=1.0)

The states are padded with a chosen value. Usually this value is set to one. The padding is obtained through a vertical concatenation of the padding value and the states.

ReservoirComputing.PaddedExtendedStatesType
PaddedExtendedStates(padding)
PaddedExtendedStates(;padding=1.0)

The states are extended with the training data or predicted data and subsequently padded with a chosen value. Usually the padding value is set to one. The padding and the extension are obtained through a vertical concatenation of the padding value, the data and the states.

Non Linear Transformations

ReservoirComputing.NLAT1Type
NLAT1()

Applies the $ \text{T}_1 $ transformation algorithm, as defined in [1] and [2].

[1] Chattopadhyay, Ashesh, et al. "Data-driven prediction of a multi-scale Lorenz 96 chaotic system using a hierarchy of deep learning methods: Reservoir computing, ANN, and RNN-LSTM." (2019).

[2] Pathak, Jaideep, et al. "Model-free prediction of large spatiotemporally chaotic systems from data: A reservoir computing approach." Physical review letters 120.2 (2018): 024102.

ReservoirComputing.NLAT2Type
NLAT2()

Apply the $ \text{T}_2 $ transformation algorithm, as defined in [1].

[1] Chattopadhyay, Ashesh, et al. "Data-driven prediction of a multi-scale Lorenz 96 chaotic system using a hierarchy of deep learning methods: Reservoir computing, ANN, and RNN-LSTM." (2019).

ReservoirComputing.NLAT3Type
NLAT3()

Apply the $ \text{T}_3 $ transformation algorithm, as defined in [1].

[1] Chattopadhyay, Ashesh, et al. "Data-driven prediction of a multi-scale Lorenz 96 chaotic system using a hierarchy of deep learning methods: Reservoir computing, ANN, and RNN-LSTM." (2019).