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20232026
most citedFATA-Trans: Field And Time-Aware Transformer for Sequential Tabular Data

12 citations · 13 across the 18 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.LG2023

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…

cs.IR2023

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…

cs.LG2023★ 12 cited

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…

cs.LG2023

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…

cs.IR2023

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…

cs.LG2023

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…