Root Cause Analysis In Microservice Using Neural Granger Causal Discovery

1 code implementation • 2 Feb 2024 • Cheng-Ming Lin, Ching Chang, Wei-Yao Wang, Kuang-Da Wang, Wen-Chih Peng

To address these challenges, we propose RUN, a novel approach for root cause analysis using neural Granger causal discovery with contrastive learning.

Causal Discovery Contrastive Learning +2

TimeDRL: Disentangled Representation Learning for Multivariate Time-Series

1 code implementation • 7 Dec 2023 • Ching Chang, Chiao-Tung Chan, Wei-Yao Wang, Wen-Chih Peng, Tien-Fu Chen

Multivariate time-series data in numerous real-world applications (e. g., healthcare and industry) are informative but challenging due to the lack of labels and high dimensionality.

Inductive Bias Representation Learning +4

LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

no code implementations • 16 Aug 2023 • Ching Chang, Wei-Yao Wang, Wen-Chih Peng, Tien-Fu Chen

Recently, researchers have leveraged the representation learning transferability of pre-trained Large Language Models (LLMs) to handle limited non-linguistic datasets effectively.

Chatbot Multivariate Time Series Forecasting +4

Detecting and Ranking Causal Anomalies in End-to-End Complex System

no code implementations • 18 Jan 2023 • Ching Chang, Wen-Chih Peng

By collecting a large amount of machine sensor data, we can have many ways to find anomalies.