REVIEW 7 cited by
Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
This systematic literature review comprehensively examines the application of Large Language Models (LLMs) in forecasting and anomaly detection, highlighting the current state of research, inherent challenges, and prospective future directions. LLMs have demonstrated significant potential in parsing and analyzing extensive datasets to identify patterns, predict future events, and detect anomalous behavior across various domains. However, this review identifies several critical challenges that impede their broader adoption and effectiveness, including the reliance on vast historical datasets, issues with generalizability across different contexts, the phenomenon of model hallucinations, limitations within the models' knowledge boundaries, and the substantial computational resources required. Through detailed analysis, this review discusses potential solutions and strategies to overcome these obstacles, such as integrating multimodal data, advancements in learning methodologies, and emphasizing model explainability and computational efficiency. Moreover, this review outlines critical trends that are likely to shape the evolution of LLMs in these fields, including the push toward real-time processing, the importance of sustainable modeling practices, and the value of interdisciplinary collaboration. Conclusively, this review underscores the transformative impact LLMs could have on forecasting and anomaly detection while emphasizing the need for continuous innovation, ethical considerations, and practical solutions to realize their full potential.
Forward citations
Cited by 7 Pith papers
-
Text-ADBench: Text Anomaly Detection Benchmark based on LLMs Embedding
LLM embeddings improve text anomaly detection, shallow detectors match deep ones only under oracle embedding selection, and AUROC matrices are low-rank enough to support fast model evaluation.
-
From Alerts to Intelligence: A Novel LLM-Aided Framework for Host-based Intrusion Detection
SHIELD, an LLM-aided pipeline combining a masked autoencoder, deterministic data augmentation, and multi-level prompting, detects host-based attacks with high precision on three public datasets.
-
A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models
The authors organize LLM-based time series reasoning into three exclusive topologies (direct, chain, branch) crossed with four objectives, and use them to label 125 papers, benchmarks, and resources.
-
Foundation Models and Transformers for Anomaly Detection: A Survey
A taxonomy and literature review of Transformer-based visual anomaly detection, compromised by fabricated citations with dummy arXiv IDs.
-
Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques
A survey that maps LLM applications, vulnerabilities, and defenses across eight cybersecurity domains, but with significant citation and rigor problems.
-
Good Enough to Learn: LLM-based Anomaly Detection in ECU Logs without Reliable Labels
A decoder-only LLM pre-trained on ECU log text and fine-tuned with an entropy regularizer detects cycle-time anomalies with 0.81 region recall despite noisy labels.
-
A Survey of AIOps in the Era of Large Language Models
A systematic survey that categorizes LLM-based AIOps research into four dimensions: data sources, tasks, methods, and evaluation, claiming to be the first comprehensive such overview.
Discussion (0). Sign in to comment.