Efficient Time-Series Approximation with Linear Recurrent Neural Networks: Architecture Learning and Predictive Power

Frieder Stolzenburg, Sandra Litz, Olivia Michael +1

Recurrent neural networks are a powerful means to cope with time series. We show how autoregressive linear, i.e., linearly activated recurrent neural networks (LRNNs) can approxima…