14 papers
OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection
Seunghan Lee, Jun Seo, Jaehoon Lee +9
Time series (TS) anomaly detection (AD) lacks a universal detector, with the best choice determined by the series rather than by the algorithm. Existing methods therefore either se…
XForecast: The KDD 2026 Finance Day Challenge on News-Grounded Financial Forecasting
Jaehoon Lee, Jun Seo, Seunghan Lee +10
Financial forecasting has traditionally relied on numerical time-series (TS) signals, while textual information such as news remains largely underutilized. We introduce XForecast,…
EXAONE Finance 1.0: An Attention-free Time Series Foundation Model for Financial Time Series
Seunghan Lee, Jaehoon Lee, Jun Seo +9
This technical report presents EXAONE Forecast for Finance (EXAONE Finance), a financial time series foundation model (TSFM) tailored to financial forecasting. While recent TSFMs a…
FinVerse: Toward More Realistic Evaluation for Financial Time-Series Forecasting
Jaehoon Lee, Jun Seo, Seunghan Lee +17
Time-series foundation models (TSFMs) are increasingly evaluated on broad benchmarks that apply uniform conventional forecast evaluation metrics across heterogeneous time-series. H…
ReasonCast: Towards Explainable Time Series Forecasting with Reasoning
Seunghan Lee, Jun Seo, Jaehoon Lee +9
Most time series (TS) models are specialized for a single task, either understanding (i.e., returning text answers about a TS) or generation (i.e., returning a numeric forecast). O…
Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting
Seunghan Lee, Jaehoon Lee, Jun Seo +9
The direction of change --- whether a series will move up or down --- is often as important as its exact value in decisiondriven applications such as risk management and financial…
When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis
Hoyoung Lee, Suhwan Park, Seunghan Lee +15
Financial decision-makers face more information than they can directly inspect, making context compression necessary. Yet when large language models (LLMs) compress financial sourc…
AdaTKG: Adaptive Memory for Temporal Knowledge Graph Reasoning
Seunghan Lee, Jun Seo, Jaehoon Lee +7
Temporal knowledge graphs (TKGs) represent time-stamped relational facts and support a wide range of reasoning tasks over evolving events. However, existing methods produce entity…
CF-JEPA: Mask-free forward prediction with asymmetric encoder utilization for time-series representation learning
Jaehoon Lee, Sunghyun Sim
Self-supervised learning (SSL) for time-series representation learning is dominated by two paradigms: contrastive methods, which face challenges in constructing positive or negativ…
FinSTaR: Towards Financial Reasoning with Time Series Reasoning Models
Seunghan Lee, Jun Seo, Jaehoon Lee +7
Time series (TS) reasoning models (TSRMs) have shown promising capabilities in general domains, yet they consistently fail on financial domain, which exhibit unique characteristics…
Rethinking Multimodal Fusion for Time Series: Text Modalities Need Constrained Fusion
Seunghan Lee, Jun Seo, Jaehoon Lee +7
Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting. However, most existing metho…
FinTexTS: Financial Text-Paired Time-Series Dataset via Semantic-Based and Multi-Level Pairing
Jaehoon Lee, Suhwan Park, Taeyoon Lim +9
The financial domain involves a variety of important time-series problems. Recently, time-series analysis methods that jointly leverage textual and numerical information have gaine…
Adaptive Information Routing for Multimodal Time Series Forecasting
Jun Seo, Hyeokjun Choe, Seohui Bae +10
Time series forecasting is a critical task for artificial intelligence with numerous real-world applications. Traditional approaches primarily rely on historical time series data t…
THEME: Enhancing Thematic Investing with Semantic Stock Representations and Temporal Dynamics
Hoyoung Lee, Wonbin Ahn, Suhwan Park +6
Thematic investing, which aims to construct portfolios aligned with structural trends, remains a challenging endeavor due to overlapping sector boundaries and evolving market dynam…