A single diffusion model that works across any time series domain without domain-specific tuning
UTSD (Unified Time Series Diffusion Model) is the first unified diffusion model for time series that can model multi-domain probability distributions and generate high-quality forecasts without domain-specific tuning.
https://arxiv.org/abs/2412.03068
Original Problem 🤔:
→ Existing time series models struggle with cross-domain generalization due to varying data characteristics and distribution shifts
→ Current approaches rely heavily on statistical priors or prompt engineering which fail under domain shifts
→ Models face challenges in maintaining accuracy across different domains while avoiding error accumulation
-----
Solution in this Paper 🔧:
→ UTSD uses a condition network to capture multi-scale patterns from observation sequences
→ A denoising network reconstructs predictions from noise using conditional sampling
→ Transfer-adapter fine-tunes only 5% of parameters while preserving pre-trained knowledge
→ Performs diffusion directly in sequence space rather than latent space for better stability
→ Uses improved classifier-free guidance for conditional generation
-----
Key Insights 💡:
→ Multi-scale representation learning is crucial for cross-domain generalization
→ Direct sequence space modeling avoids error accumulation issues
→ Minimal parameter fine-tuning can adapt to new domains effectively
→ Unified architecture can handle varying data characteristics
-----
Results 📊:
→ 19.6% improvement over foundation model baselines
→ 21.2% improvement over proprietary domain-specific models
→ Superior zero-shot generalization across all tested domains
→ Stable and reliable generation with single sampling
Share this post