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TimeWak: Temporal Chained-Hashing Watermark for Time Series Data

NeurIPS 2025 · Time series › Causal and generative time series · approach: State-space and latent dynamics forecasting · 1 citations (OpenAlex)

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Proposes TimeWak, a generation-time watermark for multivariate time series diffusion models that embeds temporally chained-hashed seeds in data space and detects them via epsilon-exact BDIA-DDIM inversion.

Problem

Watermarking synthetic multivariate time series produced by data-space diffusion models, where latent-space watermarks are inapplicable and feature heterogeneity and temporal dependence make detection hard under lossy diffusion inversion.

Approach

Embed watermark seeds as temporally chained hashes along the time axis, shuffle them per feature, map them to initial Gaussian noise, and detect by inverting a BDIA-DDIM sampler with a bounded inversion error (epsilon-exact inversion) and checking hash consistency.

Key results

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use TimeWak instead of TabWakᵀDiffusion-TS watermarking · MuJoCoContext-FID: 61.96% betterHelped
Use TimeWak instead of TabWakᵀDiffusion-TS watermarking · fMRICorrelational: 8.44% betterHelped
Use TimeWak instead of the strongest baselineDiffusion-TS watermarking · Stocks, ETTh, MuJoCo, Energy and fMRIZ-scoreMixed

Surprising findings

Limitations

Experimental setup

Datasets: Stocks ETTh MuJoCo Energy fMRI

Benchmarks: Stocks ETTh MuJoCo Energy fMRI

Metrics: Context-FID Correlational score Discriminative score Predictive score Z-score TPR@0.1%FPR

Baselines: Tree-Ring Gaussian Shading TabWak TabWak-transposed Heads Tails Watermark

Builds on: Diffusion-TS DDIM BDIA Gaussian Shading TabWak

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