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Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting

arXiv – CS AI|Yixin Wang, Yifan Hu, Peiyuan Liu, Naiqi Li, Dai Tao, Shu-Tao Xia|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce TimeGS, a novel time series forecasting framework that reimagines prediction as 2D generative rendering using Gaussian splatting techniques. The approach addresses key limitations in existing methods by treating future sequences as continuous latent surfaces and enforcing temporal continuity across periodic boundaries.

Key Takeaways

→TimeGS shifts time series forecasting from traditional regression to 2D generative rendering paradigm.
→The framework uses Gaussian kernels to adaptively model complex temporal variations with flexible geometric alignment.
→Multi-Basis Gaussian Kernel Generation stabilizes optimization by synthesizing kernels from a fixed dictionary.
→Multi-Period Chronologically Continuous Rasterization ensures strict temporal continuity across periodic boundaries.
→Comprehensive experiments demonstrate state-of-the-art performance on standard benchmark datasets.

#time-series #forecasting #gaussian-splatting #machine-learning #research #arxiv #temporal-modeling #2d-rendering

Read Original →via arXiv – CS AI

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