{"total":15,"items":[{"citing_arxiv_id":"2606.27561","ref_index":58,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Quantum Generative Diffusion Model for Real-World Time Series","primary_cat":"cs.LG","submitted_at":"2026-06-25T21:33:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"QDiffusion-TS is the first quantum generative diffusion model for time series, achieving ~44% lower Wasserstein distance on Apple and Amazon stock data and up to 71% better forecasting RMSE with ~1000x fewer parameters than classical diffusion.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00708","ref_index":50,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MOSAIC: Modular Orchestration for Structured Agentic Intelligence and Composition","primary_cat":"cs.AI","submitted_at":"2026-05-30T12:31:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"MOSAIC structures LLM-based model selection via memory-grounded blueprints and failure-aware RL, reporting gains in performance and traceability on financial time-series tasks over AutoML and agent baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.07605","ref_index":13,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SRT: Super-Resolution for Time Series via Disentangled Rectified Flow","primary_cat":"cs.LG","submitted_at":"2026-05-29T10:12:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SRT decomposes low-resolution time series into trend and seasonal components, aligns them via implicit neural representations, and uses cross-resolution attention within a disentangled rectified flow to generate high-resolution outputs, with a scaled SRT-large variant for zero-shot use.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.27113","ref_index":52,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"High-Quality Synthetic Financial Time-Series using a GAN-Diffusion Framework","primary_cat":"cs.LG","submitted_at":"2026-05-26T14:52:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Hybrid CoMeTS-GAN plus diffusion model generates multivariate financial time series claimed to better reproduce stylized facts and inter-asset correlations than prior generative methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.23402","ref_index":19,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting","primary_cat":"cs.LG","submitted_at":"2026-05-22T09:13:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"PPM injects parametric structural priors into generative models via a learnable mapping to improve probabilistic forecasts on non-stationary MTS data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.28867","ref_index":36,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"PrismFlow: Residual Dynamics for Flow Matching in Time-Series Generation","primary_cat":"cs.LG","submitted_at":"2026-05-22T07:10:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PrismFlow augments flow matching with residual dynamical experts and a winner-take-all objective to reduce spectral distortion and improve mode coverage in time-series generation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.03998","ref_index":25,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"TGSD: Topology-Guided State-Space Diffusion Framework for EEG Spatial Super-Resolution","primary_cat":"eess.SP","submitted_at":"2026-05-22T04:14:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"TGSD combines a Hierarchical Spatial Prior Encoder with conditional state-space diffusion to achieve EEG spatial super-resolution, outperforming baselines on reconstruction fidelity and classification on SEED and PhysioNet datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.14422","ref_index":17,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"What if Tomorrow is the World Cup Final? Counterfactual Time Series Forecasting with Textual Conditions","primary_cat":"cs.LG","submitted_at":"2026-05-14T06:10:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Introduces the task of counterfactual time series forecasting with textual conditions plus a text-attribution mechanism that improves accuracy by distinguishing mutable from immutable factors.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.05736","ref_index":29,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"SDFlow: Similarity-Driven Flow Matching for Time Series Generation","primary_cat":"cs.AI","submitted_at":"2026-05-07T06:28:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SDFlow learns a global transport map via similarity-driven flow matching in VQ latent space, using low-rank manifold decomposition and a categorical posterior to handle discreteness, yielding SOTA long-horizon performance and inference speedups.","context_count":1,"top_context_role":"baseline","top_context_polarity":"baseline","context_text":",xℓ)∈R ℓ×d denote a multivariate time series of length ℓwithdobserved dimensions. The unconditional generation problem is defined as: Input:Z 0 ∼π 0;Output: ˆX1:ℓ =G(Z 0)∈R ℓ×d,(1) where Z0 ∈R ℓ×d, π0 =N(0,I) is a tractable prior, and G maps samples from the prior to the target data distribution. The generative model can be implemented using GANs [29], V AEs [9], or diffusion models [30], which effectively capture complex temporal dependencies. During training, G is optimized to minimize discrepancy between generated ˆX1:ℓ and real data X1:ℓ, ensuring high fidelity and diversity in generated sequences. Similarity-Driven Vector Quantization.Following [ 7], we employ a VQ-V AE tokenizer that maps continuous time series to discrete token sequences."},{"citing_arxiv_id":"2604.20288","ref_index":140,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework","primary_cat":"cs.LG","submitted_at":"2026-04-22T07:35:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Hyperparameter-optimized generative models augment scarce flight diversion records and substantially improve prediction accuracy over real data alone.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.05257","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation","primary_cat":"cs.LG","submitted_at":"2026-04-06T23:46:40+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A temporal extension of TabDDPM generates coherent synthetic time-series sequences on the WISDM dataset that match real distributions and support downstream classification with macro F1 of 0.64.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.08318","ref_index":106,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Is Flow Matching Just Trajectory Replay for Sequential Data?","primary_cat":"stat.ML","submitted_at":"2026-02-09T06:48:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Flow matching on time series targets a closed-form nonparametric velocity field that is a similarity-weighted mixture of observed transition velocities, making neural models approximations to an ideal memory-augmented dynamical system sampler.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2512.15067","ref_index":39,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"EMFusion: Uncertainty-Aware Conditional Diffusion Model for Multivariate Narrow-band Exposure Forecasting","primary_cat":"cs.LG","submitted_at":"2025-12-17T04:12:52+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2505.14202","ref_index":26,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"MSDformer: Multi-scale Discrete Transformer For Time Series Generation","primary_cat":"cs.LG","submitted_at":"2025-05-20T11:01:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"MSDformer introduces a multi-scale discrete transformer that tokenizes time series at multiple scales and models them autoregressively in discrete space, claiming superior performance over prior DTM methods with rate-distortion theoretical support.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2505.04278","ref_index":11,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Non-stationary Diffusion For Probabilistic Time Series Forecasting","primary_cat":"cs.LG","submitted_at":"2025-05-07T09:29:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"NsDiff combines a denoising diffusion conditional generative model with a pre-trained mean/variance estimator and an uncertainty-aware noise schedule based on the Location-Scale Noise Model to capture time-varying uncertainty in probabilistic forecasting.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}