Democratizing Predictive Analytics with Generative Artificial Intelligence towards AI-Native Networks
Bibliographic record
Abstract
In the rapidly advancing era of Artificial Intelligence (AI), the transformative force of foundation models has streamlined the automated generation of multi-modal content, corresponding to user intents. At the same time, Machine Learning (ML), particularly Deep Learning (DL), has achieved state-of-the-art performance in optimization and inference tasks of various domains, including telecommunications. However, the segregation of technological domains poses challenges to the integration of powerful AI/ML capabilities towards realizing the vision of "AI-native" networks, such as future 6G networks that will provide ubiquitous intelligence across their infrastructure and service planes and will seamlessly adapt and evolve to support new classes of applications. This paper introduces Auto-TimeGPT, a handsfree Automated ML (AutoML) solution, extending the profound impact of Generative AI (GenAI) for Time Series Analysis, to networks and communications. The contribution includes an out-of-the-box zero-shot approach for anomaly detection and Time Series Forecasting (TSF) which is particularly useful for the Edge-Cloud orchestration framework CODECO, comprehensive in and out-of-domain evaluation, and a limitations analysis. Evaluation results across diverse real-world datasets demonstrate competitive performance with cutting-edge approaches based on Large Language Models (LLMs) and DL models, generalization and optimization ability and effective anomaly detection. The proposed framework democratizes AI analytics in communication and networking domains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".