5 papers
MixBench-TS: A Multivariate Time Series Forecasting Benchmark Where Channel Mixing Pays Off
Ibram Abdelmalak, Mischa Putzke, Jungmin Choi +3
Multivariate Time Series Forecasting (MTSF) models that mix information across channels assume that the past of one channel carries information about the future of another. Yet the…
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…
NPMixer: Hierarchical Neighboring Patch Mixing for Time Series Forecasting
Jung Min Choi, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme
Multivariate time series forecasting remains a challenge due to the complexity of local temporal dynamics and global dependencies across multiple variables. In this paper, we propo…
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…
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…