32 citations · 42 across the 10 of their papers we have counts for
11 papers
TimeSage-EV: A Live Benchmark for Agentic Time Series Analysis in Evolving Environments
Qingren Yao, Yaxuan Kong, Yuqi Nie +6
Time series analysis in high-stakes domains relies on recurring data releases, where new observations can alter the evidence base and the validity of later conclusions. Existing ti…
TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning
Yaxuan Kong, Qingren Yao, Yuqi Nie +7
Time series data inform critical decisions across many real-world domains. While large language model (LLM) agents can analyze data through natural language and tools, it remains u…
Evaluating LLMs in Finance Requires Explicit Bias Consideration
Yaxuan Kong, Hoyoung Lee, Yoontae Hwang +7
Large Language Models (LLMs) are increasingly integrated into financial workflows, but evaluation practice has not kept up. Finance-specific biases can inflate performance, contami…
Fusing Narrative Semantics for Financial Volatility Forecasting
Yaxuan Kong, Yoontae Hwang, Marcus Kaiser +3
We introduce M2VN: Multi-Modal Volatility Network, a novel deep learning-based framework for financial volatility forecasting that unifies time series features with unstructured ne…
Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement
Yaxuan Kong, Yiyuan Yang, Yoontae Hwang +5
Time series data are foundational in finance, healthcare, and energy domains. However, most existing methods and datasets remain focused on a narrow spectrum of tasks, such as fore…
Decision-informed Neural Networks with Large Language Model Integration for Portfolio Optimization
Yoontae Hwang, Yaxuan Kong, Stefan Zohren +1
This paper addresses the critical disconnect between prediction and decision quality in portfolio optimization by integrating Large Language Models (LLMs) with decision-focused lea…
Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluation
Yaxuan Kong, Yiyuan Yang, Shiyu Wang +7
Understanding time series data is fundamental to many real-world applications. Recent work explores multimodal large language models (MLLMs) to enhance time series understanding wi…
Extracting Alpha from Financial Analyst Networks
Dragos Gorduza, Yaxuan Kong, Xiaowen Dong +1
We investigate the effectiveness of a momentum trading signal based on the coverage network of financial analysts. This signal builds on the key information-brokerage role financia…
Unlocking the Power of LSTM for Long Term Time Series Forecasting
Yaxuan Kong, Zepu Wang, Yuqi Nie +5
Traditional recurrent neural network architectures, such as long short-term memory neural networks (LSTM), have historically held a prominent role in time series forecasting (TSF)…
A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges
Yuqi Nie, Yaxuan Kong, Xiaowen Dong +4
Recent advances in large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. These models have demonstrated remarkab…
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
Ming Jin, Yaxuan Kong, Yuxuan Liang +13
Temporal data, including time series and spatio-temporal data, are pervasive in real-world applications. Generated in massive volumes by physical and virtual sensors, they record d…