arXiv subjects

Hyungjin Park

Publications and source records attributed to Hyungjin Park.

3 recordsLinked to original sources

arXiv2026

What If TSF: Reframing Time Series Forecasting as Scenario-Guided Multimodal Forecasting

Recent advances in large language models (LLMs) have enabled time series forecasting to move beyond numerical observations and incorporate external information in multimodal settings. Such information can improve forecasting performance, but accuracy alone may not reveal whether models appropriately respond to it: a model may ignore relevant information, fail to distinguish scenarios with different implications, or overreact to irrelevant or weak signals. We introduce What If TSF (WIT), a benchmark for evaluating whether models effectively respond to future scenarios. WIT constructs controlled scenario sets by fixing the forecasting state while systematically varying future scenarios, enabling three complementary evaluations: Factual Comparison, which measures the predictive utility of factual scenario information; Relational Comparison, which evaluates whether forecasts satisfy expected direction, contrast, intensity, and restraint relations across scenarios; and Grounded Comparison, which assesses whether scenario-induced revision trajectories are empirically plausible relative to comparable real cases. Experiments show that factual accuracy gains do not consistently translate into appropriate responses to alternative scenarios, demonstrating the need to evaluate multimodal forecasting beyond predictive accuracy.

cs.AI↗

arXiv2026

Preserve and Personalize: Personalized Text-to-Image Diffusion Models without Distributional Drift

Personalizing text-to-image diffusion models involves integrating novel visual concepts from a small set of reference images while retaining the model's original generative capabilities. However, this process often leads to overfitting, where the model ignores the user's prompt and merely replicates the reference images. We attribute this issue to a fundamental misalignment between the true goals of personalization, which are subject fidelity and text alignment, and the training objectives of existing methods that fail to enforce both objectives simultaneously. Specifically, prior approaches often overlook the need to explicitly preserve the pretrained model's output distribution, resulting in distributional drift that undermines diversity and coherence. To resolve these challenges, we introduce a Lipschitz-based regularization objective that constrains parameter updates during personalization, ensuring bounded deviation from the original distribution. This promotes consistency with the pretrained model's behavior while enabling accurate adaptation to new concepts. Furthermore, our method offers a computationally efficient alternative to commonly used, resource-intensive sampling techniques. Through extensive experiments across diverse diffusion model architectures, we demonstrate that our approach achieves superior performance in both quantitative metrics and qualitative evaluations, consistently excelling in visual fidelity and prompt adherence. We further support these findings with comprehensive analyses, including ablation studies and visualizations.

cs.CV↗

arXiv2026

Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependency, Asynchrony, and Missingness

Real-world time series data are inherently multivariate, often exhibiting complex inter-channel dependencies. Each channel is typically sampled at its own period and is prone to missing values due to various practical and operational constraints. These characteristics pose three fundamental challenges involving channel dependency, sampling asynchrony, and missingness, all of which must be addressed simultaneously to enable robust and reliable forecasting in practical settings. However, existing architectures typically address only parts of these challenges in isolation and still rely on simplifying assumptions, leaving unresolved the combined challenges of asynchronous channel sampling, test-time missing blocks, and intricate inter-channel dependencies. To bridge this gap, we propose ChannelTokenFormer, a Transformer-based forecasting framework with a flexible architecture designed to explicitly capture cross-channel interactions, accommodate channel-wise asynchronous sampling, and effectively handle missing values. Extensive experiments on public benchmark datasets reflecting practical settings, along with one private real-world industrial dataset, demonstrate the superior robustness and accuracy of ChannelTokenFormer under challenging real-world conditions.

cs.LG↗