Fractional Tensor Recurrent Unit (fTRU): A Stable Forecasting Model With Long Memory.

The tensor recurrent model is a family of nonlinear dynamical systems, of which the recurrence relation consists of a -fold (called degree- ) tensor product. Despite such models frequently appearing in advanced recurrent neural networks (RNNs), to this date, there are limited studies on their long memory properties and stability in sequence tasks. In this article, we propose a fractional tensor recurrent model, where the tensor degree is extended from the discrete domain to the continuous domain, so it is effectively learnable from various datasets.