A Hybrid Time Series Forecasting Model for Disturbance Storm Time Index using a Competitive Brain Emotional Neural Network and Neo-Fuzzy Neurons
Why this work is in the frame
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Bibliographic record
Abstract
The Disturbance storm time (Dst) index is an important indicator of the occurrence of geomagnetic storms, which can damage communication and power systems, as well as, affect Astronauts performance. Such potential consequences of this fatal event has challenged researchers to develop Dst predictors, with some success. This paper presents the design of a computationally fast, neuro-fuzzy network to forecast Dst activity. The proposed network combines a class of emotional neural networks with neo-fuzzy neurons and is named, Neo-fuzzy integrated Competitive Brain Emotional Learning (NFCBEL) network. Equipped with five competing units, the hybrid model accepts only the past two samples of Dst time series, to predict future values. The model has been tested in the MATLAB programming environment and has been found to offer superior performance, as compared to other state-of-the-art Dst predictors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it