15 citations · 27 across the 13 of their papers we have counts for
13 papers
Empowering Time Series Forecasting with LLM-Agents
Chin-Chia Michael Yeh, Vivian Lai, Uday Singh Saini +5
Large Language Model (LLM) powered agents have emerged as effective planners for Automated Machine Learning (AutoML) systems. While most existing AutoML approaches focus on automat…
Towards Efficient Large Scale Spatial-Temporal Time Series Forecasting via Improved Inverted Transformers
Jiarui Sun, Chin-Chia Michael Yeh, Yujie Fan +10
Time series forecasting at scale presents significant challenges for modern prediction systems, particularly when dealing with large sets of synchronized series, such as in a globa…
UltraSTF: Ultra-Compact Model for Large-Scale Spatio-Temporal Forecasting
Chin-Chia Michael Yeh, Xiran Fan, Zhimeng Jiang +9
Spatio-temporal data, prevalent in real-world applications such as traffic monitoring, financial transactions, and ride-share demands, represents a specialized case of multivariate…
Matrix Profile for Anomaly Detection on Multidimensional Time Series
Chin-Chia Michael Yeh, Audrey Der, Uday Singh Saini +11
The Matrix Profile (MP), a versatile tool for time series data mining, has been shown effective in time series anomaly detection (TSAD). This paper delves into the problem of anoma…
A Systematic Evaluation of Generated Time Series and Their Effects in Self-Supervised Pretraining
Audrey Der, Chin-Chia Michael Yeh, Xin Dai +9
Self-supervised Pretrained Models (PTMs) have demonstrated remarkable performance in computer vision and natural language processing tasks. These successes have prompted researcher…
Masked Graph Transformer for Large-Scale Recommendation
Huiyuan Chen, Zhe Xu, Chin-Chia Michael Yeh +4
Graph Transformers have garnered significant attention for learning graph-structured data, thanks to their superb ability to capture long-range dependencies among nodes. However, t…
RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data
Chin-Chia Michael Yeh, Yujie Fan, Xin Dai +9
Spatial-temporal forecasting systems play a crucial role in addressing numerous real-world challenges. In this paper, we investigate the potential of addressing spatial-temporal fo…
Towards Mitigating Dimensional Collapse of Representations in Collaborative Filtering
Huiyuan Chen, Vivian Lai, Hongye Jin +3
Contrastive Learning (CL) has shown promising performance in collaborative filtering. The key idea is to generate augmentation-invariant embeddings by maximizing the Mutual Informa…
Ego-Network Transformer for Subsequence Classification in Time Series Data
Chin-Chia Michael Yeh, Huiyuan Chen, Yujie Fan +8
Time series classification is a widely studied problem in the field of time series data mining. Previous research has predominantly focused on scenarios where relevant or foregroun…
Temporal Treasure Hunt: Content-based Time Series Retrieval System for Discovering Insights
Chin-Chia Michael Yeh, Huiyuan Chen, Xin Dai +8
Time series data is ubiquitous across various domains such as finance, healthcare, and manufacturing, but their properties can vary significantly depending on the domain they origi…
An Efficient Content-based Time Series Retrieval System
Chin-Chia Michael Yeh, Huiyuan Chen, Xin Dai +9
A Content-based Time Series Retrieval (CTSR) system is an information retrieval system for users to interact with time series emerged from multiple domains, such as finance, health…
Toward a Foundation Model for Time Series Data
Chin-Chia Michael Yeh, Xin Dai, Huiyuan Chen +8
A foundation model is a machine learning model trained on a large and diverse set of data, typically using self-supervised learning-based pre-training techniques, that can be adapt…
Sharpness-Aware Graph Collaborative Filtering
Huiyuan Chen, Chin-Chia Michael Yeh, Yujie Fan +5
Graph Neural Networks (GNNs) have achieved impressive performance in collaborative filtering. However, GNNs tend to yield inferior performance when the distributions of training an…