  Machine learning methods trained on raw numerical time series data exhibit
fundamental limitations such as a high sensitivity to the hyper parameters and
even to the initialization of random weights. A combination of a recurrent
neural network with a dimension-reducing symbolic representation is proposed
and applied for the purpose of time series forecasting. It is shown that the
symbolic representation can help to alleviate some of the aforementioned
problems and, in addition, might allow for faster training without sacrificing
the forecast performance.
