Prediction of multidimensional time-series data using a recurrent neural network (RNN) trained by real-time recurrent learning (RTRL), unbiased online recurrent optimization (UORO), least mean squares ...

Ensemble methods used for classification and regression have been shown that they are superior than other methods, teoritically and empirically. Adapting this method on time-series prediction is done ...

To use a Recurrent Neural Network (RNN) for time series modeling, it is essential to properly initialize the network, that is, to set the hidden neuron outputs properly at the initial time. Normally, ...

Recurrent neural networks (RNNs) have been foundational in machine learning for addressing various sequence-based problems, including time series forecasting and natural language processing. RNNs are ...

Prediction of multidimensional time-series data using a recurrent neural network (RNN) trained by real-time recurrent learning (RTRL), unbiased online recurrent optimization (UORO), least mean squares ...

Specifically, we drive an RNN with examples of translated, linearly transformed or pre-bifurcated time series from a chaotic Lorenz system, alongside an additional control signal that changes value ...