Multivariate Machine Learning Models for Short-Term Forecast of Lightpath Performance
Bibliographic record
Abstract
Machine learning (ML) is emerging as a promising solution for managing the physical layer complexity of heterogeneous dynamic optical networks transporting multiple applications in a software defined network (SDN) context, namely for performance prediction. We propose two multivariate neural network models based on gated recurrent unit (GRU) and long short-term memory (LSTM) methods, trained with field performance data and features, for predicting lightpath signal-to-noise ratio (SNR) over forecast horizons of up to 4 days. The best performance is achieved by using a 5-feature LSTM multivariate model over forecast horizons of up to 96 hours, with an absolute maximum error (AME) of 0.90 dB, compared to 0.91 dB and 0.97 dB for the GRU and LSTM univariate models, respectively, and 1.21 dB for a persistence model. The 2-feature multivariate models obtained through feature engineering perform better than their univariate counterparts for forecast horizons of up to 40 hours. Lastly, we explore the concept of transfer learning (TL) by testing the trained multivariate LSTM and univariate GRU models on field data from two lightpaths carried on the same route. The TL models underperform the naive model for the lightpath carried in a different optical fiber. However, for the lightpath carried in the same optical fiber on a portion of the same route, the LSTM-based TL model outperforms the naive model with a difference of up to 0.11 dB at a 96-hour forecast horizon, compared to 0.30 dB for the lightpath in the source domain, while using 3 times less training data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".