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The Rise of Diffusion Models in Time-Series Forecasting
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This survey delves into the application of diffusion models in time-series forecasting. Diffusion models are demonstrating state-of-the-art results in various fields of generative AI. The paper includes comprehensive background information on diffusion models, detailing their conditioning methods and reviewing their use in time-series forecasting. The analysis covers 11 specific time-series implementations, the intuition and theory behind them, the effectiveness on different datasets, and a comparison among each other. Key contributions of this work are the thorough exploration of diffusion models' applications in time-series forecasting and a chronologically ordered overview of these models. Additionally, the paper offers an insightful discussion on the current state-of-the-art in this domain and outlines potential future research directions. This serves as a valuable resource for researchers in AI and time-series analysis, offering a clear view of the latest advancements and future potential of diffusion models.
Forward citations
Cited by 8 Pith papers
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EMFusion: Uncertainty-Aware Conditional Diffusion Model for Multivariate Narrow-band Exposure Forecasting
On an 8-month Italian hospital EMF dataset, a working-hour-conditioned multivariate diffusion forecaster beats the best baseline in CRPS and NRMSE, though the headline percentages do not match the paper's own aggregate table.
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DeXposure-FM: A Time-series, Graph Foundation Model for Credit Exposures and Stability on Decentralized Financial Networks
A fine-tuned GraphPFN foundation model forecasts DeFi exposure networks and stress-test losses, beating learned baselines everywhere and persistence on link statistics, though not on average stress-test error.
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Hallucination Detection and Mitigation with Diffusion in Multi-Variate Time-Series Foundation Models
Pre-trained multivariate time-series imputation models frequently return values that violate known relations between variables, and a diffusion-based score can detect and filter these errors.
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CSI Prediction Using Diffusion Models
Diffusion-based CSI predictors conditioned on temporal encoders report NMSE gains of 5–8 dB over GRU, ConvLSTM, and LinFormer baselines in 3GPP CDL simulations.
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Conditional Deep Levy Models for Exotic Derivatives: History-Aware Path Generation and P-Q Payoff Diagnostics
The abstract presents a history-aware diffusion path generator and P-Q payoff diagnostic with reported CRPS improvements, but the body does not contain the corresponding method or results.
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RDIT: Residual-based Diffusion Implicit Models for Probabilistic Time Series Forecasting
RDIT adds residual diffusion and variance calibration on top of a strong point forecaster, achieving best CRPS on seven of eight datasets and lower PICP distance in most settings.
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From Vector Autoregressions to AI-based Time Series Forecasting: A Review
AI forecasting methods are flexible generalizations of the classical VAR's conditional forecast distribution, gaining adaptability and scale but losing ready-made inference, identification, and structural interpretation.
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Diffusion Models for Time Series Forecasting: A Survey
A survey classifies diffusion-based time series forecasting models into a two-axis taxonomy by conditioning source and integration method.
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