3 papers
cs.LG2026
CAMP: A Cycle-Aware Multi-Scale Patch Mixer for Time Series Forecasting
Jung Min Choi, Vijaya Krishna yalavarthi, Lars Schmidt-Thieme
Real-world time series are often governed by recurring patterns, but their dominant periods may vary across datasets, forecasting settings, and individual input windows. Existing c…
cs.LG2026
HPMixer: Hierarchical Patching for Multivariate Time Series Forecasting
Jung Min Choi, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme
In long-term multivariate time series forecasting, effectively capturing both periodic patterns and residual dynamics is essential. To address this within standard deep learning be…
cs.LG2025
Channel Dependence, Limited Lookback Windows, and the Simplicity of Datasets: How Biased is Time Series Forecasting?
Ibram Abdelmalak, Kiran Madhusudhanan, Jungmin Choi +4
In Long-term Time Series Forecasting (LTSF), the lookback window is a critical hyperparameter often set arbitrarily, undermining the validity of model evaluations. We argue that th…