With ever-increasing traffic, the need more dynamic, flexible and autonomous optical networks is more important than ever. The availability of performance monitoring data makes it possible to leverage machine learning (ML) for fast quality of transmission (QoT) estimation and performance prediction of lightpaths in complex optical networks. In this work, we explore classifiers based on support vector machine (SVM) and artificial neural network (ANN) for QoT estimation of unestablished lightpaths. Using a synthetic knowledge base (KB), the classification accuracy of the ANN and SVM models decreased from 99%, with a complete feature set, to 85.03% and 88.52%, respectively, with a reduced feature set. We also propose a Long Short-Term Memory (LSTM), an Encoder-Decoder LSTM and a Gated Recurrent Unit (GRU) models, trained with 13-month field performance data, for lightpath signal-to-noise (SNR) prediction over forecast horizons up to 4 days. Positive R 2 values combined with low ( 0.285 dB) root mean square error (RMSE) indicated that the GRU model achieved slightly better predictions than the naive method for forecast horizons ranging from 1 to 96 hours, whereas the LSTM performed better over 24 to 96-hour forecast horizons. The Encoder-Decoder LSTM model achieved the lowest R 2 and the highest RMSE values (0.296 dB). Additional input data will be needed to improve the prediction accuracy of the LSTM and GRU models trained with single lightpath data.

We show how the Recurrent Neural Networks can be used for performance prediction of lightpaths using field bit error rate data. Moreover, we illustrate how the forecast horizons and observation windows affect the forecast accuracy.