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

2026-06-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

TimeGS is a novel framework for time series forecasting (TSF) that redefines the problem from regression to 2D generative rendering, addressing limitations of existing 2D period-phase representations. Previous methods, like TimesNet and PDF, suffer from topological mismatches that sever chronological continuity and inefficient fixed-size representations. TimeGS conceptualizes future sequences as continuous latent surfaces, using anisotropic Gaussian kernels for adaptive modeling. It introduces a Multi-Basis Gaussian Kernel Generation (MB-GKG) block to stabilize optimization via a fixed dictionary and a Multi-Period Chronologically Continuous Rasterization (MP-CCR) block to ensure temporal continuity across periodic boundaries. Comprehensive experiments on 7 standard benchmark datasets, with an input length of 96 and prediction horizons up to 720, demonstrate TimeGS achieves state-of-the-art performance, particularly on datasets with strong periodicity like Electricity and Traffic.

Key takeaway

For machine learning engineers developing advanced time series forecasting models, you should explore generative rendering paradigms like TimeGS to overcome limitations of traditional regression. By reconceptualizing future sequences as continuous latent surfaces and employing 2D Gaussian splatting, you can better capture intricate intraperiod- fluctuations and interperiod- trends. Consider integrating multi-basis kernel generation and chronologically continuous rasterization to enhance model stability and ensure temporal coherence, especially for datasets with strong periodicities.

Key insights

Time series forecasting can be fundamentally improved by treating it as 2D generative rendering using anisotropic Gaussian splatting.

Principles

• 2D period-phase representations require continuous temporal handling.
• Fixed basis banks stabilize geometric parameter optimization.
• Channel-adaptive weighting improves multivariate forecasting.

Method

TimeGS reshapes 1D series to 2D, extracts features with UNet-based encoders, generates Gaussian kernels from a fixed basis bank, rasterizes them with continuous temporal wrapping, and fuses forecasts via channel-adaptive weighting.

In practice

• Consider 2D Gaussian splatting for complex temporal patterns.
• Use dictionary learning for stable kernel shape generation.
• Implement channel-specific aggregation for multivariate data.

Topics

• Time Series Forecasting
• 2D Gaussian Splatting
• Generative Models
• Deep Learning
• Multivariate Forecasting
• Neural Networks

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.