"UTSD: Unified Time Series Diffusion Model"

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