78 citations · 124 across the 35 of their papers we have counts for
18 papers · 1 filter
TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates
Meysam Alishahi, Yan Zheng, Junpeng Wang +2
Tabular data generation considers a large table with multiple columns -- each column comprised of numerical, categorical, or sometimes ordinal values. The goal is to produce new ro…
TiCT: A Synthetically Pre-Trained Foundation Model for Time Series Classification
Chin-Chia Michael Yeh, Uday Singh Saini, Junpeng Wang +5
The ubiquity of time series data creates a strong demand for general-purpose foundation models, yet developing them for classification remains a significant challenge, largely due…
TREASURE: The Visa Payment Foundation Model for High-Volume Transaction Understanding
Chin-Chia Michael Yeh, Uday Singh Saini, Xin Dai +12
Payment networks form the backbone of modern commerce, generating high volumes of transaction records from daily activities. Properly modeling this data can enable applications suc…
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…
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…
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies
Audrey Der, Chin-Chia Michael Yeh, Yan Zheng +5
In recent years there has been significant progress in time series anomaly detection. However, after detecting an (perhaps tentative) anomaly, can we explain it? Such explanations…
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…
FATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data
Dongyu Zhang, Liang Wang, Xin Dai +7
Sequential tabular data is one of the most commonly used data types in real-world applications. Different from conventional tabular data, where rows in a table are independent, seq…
Multitask Learning for Time Series Data with 2D Convolution
Chin-Chia Michael Yeh, Xin Dai, Yan Zheng +7
Multitask learning (MTL) aims to develop a unified model that can handle a set of closely related tasks simultaneously. By optimizing the model across multiple tasks, MTL generally…
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…
EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding
Yan Zheng, Junpeng Wang, Chin-Chia Michael Yeh +4
Embedding learning transforms discrete data entities into continuous numerical representations, encoding features/properties of the entities. Despite the outstanding performance re…
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…
How Does Attention Work in Vision Transformers? A Visual Analytics Attempt
Yiran Li, Junpeng Wang, Xin Dai +5
Vision transformer (ViT) expands the success of transformer models from sequential data to images. The model decomposes an image into many smaller patches and arranges them into a…
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series
Audrey Der, Chin-Chia Michael Yeh, Renjie Wu +6
The most useful data mining primitives are distance measures. With an effective distance measure, it is possible to perform classification, clustering, anomaly detection, segmentat…