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cs.LG2026

FinVerse: Financial Time-Series Benchmark

Jaehoon Lee, Jun Seo, Seunghan Lee +9

As time-series foundation models have emerged, the need for benchmarks that can evaluate their forecasting ability in meaningful ways has become increasingly important. Existing ti…

cs.LG2026

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…

cs.LG2026

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…

cs.LG2026

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…

cs.LG2025

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…