Search Results for author:
Found 19 papers, 11 papers with code
ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning
To the best of our knowledge, ChatTS is the first MLLM that takes multivariate time series as input, which is fine-tuned exclusively on synthetic datasets.
KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks
Due to the common occurrence of noise, i. e., local peaks and drops in time series, existing black-box learning methods can easily learn these unintended patterns, significantly affecting anomaly detection performance.
Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Model Based on Large Language Models
Large language models (LLMs) excel at general question-answering (Q&A) but often fall short in specialized domains due to a lack of domain-specific knowledge.
A Scenario-Oriented Benchmark for Assessing AIOps Algorithms in Microservice Management
The core idea is to build a live microservice benchmark to generate real-time datasets and consistently simulate the specific operation scenarios on it.
LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysis
These findings provide insights into the strengths and weaknesses of LLMs in multilingual environments and the effectiveness of different prompt strategies.
TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models
The performance of testing newly incoming unseen time series on current TSAD algorithms remains unknown.
Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective
To ensure an accurate AD, FCVAE exploits an innovative approach to concurrently integrate both the global and local frequency features into the condition of Conditional Variational Autoencoder (CVAE) to significantly increase the accuracy of reconstructing the normal data.
OpsEval: A Comprehensive IT Operations Benchmark Suite for Large Language Models
Information Technology (IT) Operations (Ops), particularly Artificial Intelligence for IT Operations (AIOps), is the guarantee for maintaining the orderly and stable operation of existing information systems.
Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection
Massive key performance indicators (KPIs) are monitored as multivariate time series data (MTS) to ensure the reliability of the software applications and service system.
A Survey of Time Series Anomaly Detection Methods in the AIOps Domain
Internet-based services have seen remarkable success, generating vast amounts of monitored key performance indicators (KPIs) as univariate or multivariate time series.
LogKG: Log Failure Diagnosis Through Knowledge Graph
To fully use the information represented by KG, we propose a failure-oriented log representation (FOLR) method to extract the failure-related patterns.
Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systems
Such attribute combinations are substantial clues to the underlying root causes and thus are called root causes of multidimensional data.
Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognition
Fault diagnosis is critical in many domains, as faults may lead to safety threats or economic losses.
UniLog: Deploy One Model and Specialize it for All Log Analysis Tasks
UniLog: Deploy One Model and Specialize it for All Log Analysis Tasks
DOI: Divergence-based Out-of-Distribution Indicators via Deep Generative Models
We propose a novel theoretical framework, DOI, for divergence-based Out-of-Distribution indicators (instead of traditional likelihood-based) in deep generative models.
The Search for Sparse, Robust Neural Networks
Recent work on deep neural network pruning has shown there exist sparse subnetworks that achieve equal or improved accuracy, training time, and loss using fewer network parameters when compared to their dense counterparts.
Shallow VAEs with RealNVP Prior Can Perform as Well as Deep Hierarchical VAEs
Using powerful posterior distributions is a popular technique in variational inference.
On the Necessity and Effectiveness of Learning the Prior of Variational Auto-Encoder
Using powerful posterior distributions is a popular approach to achieving better variational inference.
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e. g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation.
