14 citations · 54 across the 32 of their papers we have counts for
32 papers
TransactionGPT
Yingtong Dou, Zhimeng Jiang, Tianyi Zhang +26
We present TransactionGPT (TGPT), a foundation model for consumer transaction data within one of the world's largest payment networks. TGPT is designed to understand and generate t…
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…
Preserving Individuality while Following the Crowd: Understanding the Role of User Taste and Crowd Wisdom in Online Product Rating Prediction
Liang Wang, Shubham Jain, Yingtong Dou +9
Numerous algorithms have been developed for online product rating prediction, but the specific influence of user and product information in determining the final prediction score r…
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…
Has Your Pretrained Model Improved? A Multi-head Posterior Based Approach
Prince Aboagye, Yan Zheng, Junpeng Wang +8
The emergence of pre-trained models has significantly impacted Natural Language Processing (NLP) and Computer Vision to relational datasets. Traditionally, these models are assesse…
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…
Sketching Multidimensional Time Series for Fast Discord Mining
Chin-Chia Michael Yeh, Yan Zheng, Menghai Pan +7
Time series discords are a useful primitive for time series anomaly detection, and the matrix profile is capable of capturing discord effectively. There exist many research efforts…
Time Series Synthesis Using the Matrix Profile for Anonymization
Audrey Der, Chin-Chia Michael Yeh, Yan Zheng +6
Publishing and sharing data is crucial for the data mining community, allowing collaboration and driving open innovation. However, many researchers cannot release their data due to…
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…
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…
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…
PDT: Pretrained Dual Transformers for Time-aware Bipartite Graphs
Xin Dai, Yujie Fan, Zhongfang Zhuang +7
Pre-training on large models is prevalent and emerging with the ever-growing user-generated content in many machine learning application categories. It has been recognized that lea…
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…
Quantized Wasserstein Procrustes Alignment of Word Embedding Spaces
Prince O Aboagye, Yan Zheng, Michael Yeh +6
Optimal Transport (OT) provides a useful geometric framework to estimate the permutation matrix under unsupervised cross-lingual word embedding (CLWE) models that pose the alignmen…
Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph
Chin-Chia Michael Yeh, Mengting Gu, Yan Zheng +6
Graph neural networks (GNNs) are deep learning models designed specifically for graph data, and they typically rely on node features as the input to the first layer. When applying…
One-Shot Learning on Attributed Sequences
Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner +2
One-shot learning has become an important research topic in the last decade with many real-world applications. The goal of one-shot learning is to classify unlabeled instances when…
Deep Learning on Attributed Sequences
Zhongfang Zhuang
Recent research in feature learning has been extended to sequence data, where each instance consists of a sequence of heterogeneous items with a variable length. However, in many r…
Error-bounded Approximate Time Series Joins Using Compact Dictionary Representations of Time Series
Chin-Chia Michael Yeh, Yan Zheng, Junpeng Wang +4
The matrix profile is an effective data mining tool that provides similarity join functionality for time series data. Users of the matrix profile can either join a time series with…