Renewable energy production and the balance between production and demand have become increasingly crucial in modern power systems,necessitating accurate forecasting.Traditional deterministic methods fail to capture t...Renewable energy production and the balance between production and demand have become increasingly crucial in modern power systems,necessitating accurate forecasting.Traditional deterministic methods fail to capture the inherent uncertainties associated with intermittent renewable sources and fluctuating demand patterns.This paper proposes a novel denoising diffusion method for multivariate time series probabilistic forecasting that explicitly models the interdependencies between variables through graph modeling.Our framework employs a parallel feature extraction module that simultaneously captures temporal dynamics and spatial correlations,enabling improved forecasting accuracy.Through extensive evaluation on two world real-datasets focused on renewable energy and electricity demand,we demonstrate that our approach achieves state-of-the-art performance in probabilistic energy time series forecasting tasks.By explicitly modeling variable interdependencies and incorporating temporal information,our method provides reliable probabilistic forecasts,crucial for effective decision-making and resource allocation in the energy sector.Extensive experiments validate that our proposed method reduces the Continuous Ranked Probability Score(CRPS)by 2.1%-70.9%,Mean Absolute Error(MAE)by 4.4%-52.2%,and Root Mean Squared Error(RMSE)by 7.9%-53.4%over existing methods on two real-world datasets.展开更多
Diffusion models, a family of generative models based on deep learning, have become increasinglyprominent in cutting-edge machine learning research. With distinguished performance in generating samples thatresemble th...Diffusion models, a family of generative models based on deep learning, have become increasinglyprominent in cutting-edge machine learning research. With distinguished performance in generating samples thatresemble the observed data, diffusion models are widely used in image, video, and text synthesis nowadays. Inrecent years, the concept of diffusion has been extended to time-series applications, and many powerful models havebeen developed. Considering the deficiency of a methodical summary and discourse on these models, we providethis survey as an elementary resource for new researchers in this area and to provide inspiration to motivate futureresearch. For better understanding, we include an introduction about the basics of diffusion models. Except forthis, we primarily focus on diffusion-based methods for time-series forecasting, imputation, and generation, andpresent them, separately, in three individual sections. We also compare different methods for the same applicationand highlight their connections if applicable. Finally, we conclude with the common limitation of diffusion-basedmethods and highlight potential future research directions.展开更多
Exemplar-based image translation involves converting semantic masks into photorealistic images that adopt the style of a given exemplar.However,most existing GAN-based translation methods fail to produce photorealisti...Exemplar-based image translation involves converting semantic masks into photorealistic images that adopt the style of a given exemplar.However,most existing GAN-based translation methods fail to produce photorealistic results.In this study,we propose a new diffusion model-based approach for generating high-quality images that are semantically aligned with the input mask and resemble an exemplar in style.The proposed method trains a conditional denoising diffusion probabilistic model(DDPM)with a SPADE module to integrate the semantic map.We then used a novel contextual loss and auxiliary color loss to guide the optimization process,resulting in images that were visually pleasing and semantically accurate.Experiments demonstrate that our method outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.展开更多
Single-image super-resolution(SISR)typically focuses on restoring various degraded low-resolution(LR)images to a single high-resolution(HR)image.However,during SISR tasks,it is often challenging for models to simultan...Single-image super-resolution(SISR)typically focuses on restoring various degraded low-resolution(LR)images to a single high-resolution(HR)image.However,during SISR tasks,it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture features.This challenge can lead to issues such as model collapse,lack of rich details and texture features in the reconstructed HR images,and excessive time consumption for model sampling.To address these problems,this paper proposes a Latent Feature-oriented Diffusion Probability Model(LDDPM).First,we designed a conditional encoder capable of effectively encoding LR images,reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed images.We then employed a normalized flow and multimodal adversarial training,learning from complex multimodal distributions,to model the denoising distribution.Doing so boosts the generative modeling capabilities within a minimal number of sampling steps.Experimental comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics,providing a fresh perspective for tackling SISR tasks.展开更多
文摘Renewable energy production and the balance between production and demand have become increasingly crucial in modern power systems,necessitating accurate forecasting.Traditional deterministic methods fail to capture the inherent uncertainties associated with intermittent renewable sources and fluctuating demand patterns.This paper proposes a novel denoising diffusion method for multivariate time series probabilistic forecasting that explicitly models the interdependencies between variables through graph modeling.Our framework employs a parallel feature extraction module that simultaneously captures temporal dynamics and spatial correlations,enabling improved forecasting accuracy.Through extensive evaluation on two world real-datasets focused on renewable energy and electricity demand,we demonstrate that our approach achieves state-of-the-art performance in probabilistic energy time series forecasting tasks.By explicitly modeling variable interdependencies and incorporating temporal information,our method provides reliable probabilistic forecasts,crucial for effective decision-making and resource allocation in the energy sector.Extensive experiments validate that our proposed method reduces the Continuous Ranked Probability Score(CRPS)by 2.1%-70.9%,Mean Absolute Error(MAE)by 4.4%-52.2%,and Root Mean Squared Error(RMSE)by 7.9%-53.4%over existing methods on two real-world datasets.
文摘Diffusion models, a family of generative models based on deep learning, have become increasinglyprominent in cutting-edge machine learning research. With distinguished performance in generating samples thatresemble the observed data, diffusion models are widely used in image, video, and text synthesis nowadays. Inrecent years, the concept of diffusion has been extended to time-series applications, and many powerful models havebeen developed. Considering the deficiency of a methodical summary and discourse on these models, we providethis survey as an elementary resource for new researchers in this area and to provide inspiration to motivate futureresearch. For better understanding, we include an introduction about the basics of diffusion models. Except forthis, we primarily focus on diffusion-based methods for time-series forecasting, imputation, and generation, andpresent them, separately, in three individual sections. We also compare different methods for the same applicationand highlight their connections if applicable. Finally, we conclude with the common limitation of diffusion-basedmethods and highlight potential future research directions.
基金supported in part by National Natural Science Foundation of China(U21B2023)DEGP Innovation Team(2022KCXTD025)+1 种基金Shenzhen Science and Technology Program(KQTD20210811090044003,RCJC20200714114435012)Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ).
文摘Exemplar-based image translation involves converting semantic masks into photorealistic images that adopt the style of a given exemplar.However,most existing GAN-based translation methods fail to produce photorealistic results.In this study,we propose a new diffusion model-based approach for generating high-quality images that are semantically aligned with the input mask and resemble an exemplar in style.The proposed method trains a conditional denoising diffusion probabilistic model(DDPM)with a SPADE module to integrate the semantic map.We then used a novel contextual loss and auxiliary color loss to guide the optimization process,resulting in images that were visually pleasing and semantically accurate.Experiments demonstrate that our method outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.
基金supported by General Project of Guangxi Science and Technology Major Project(AA19254016)Beihai City Science and Technology Planning Project(202082033)+1 种基金Beihai City Science and Technology Planning Project(202082023)Guangxi Graduate Student Innovation Project(YCSW2021174)。
文摘Single-image super-resolution(SISR)typically focuses on restoring various degraded low-resolution(LR)images to a single high-resolution(HR)image.However,during SISR tasks,it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture features.This challenge can lead to issues such as model collapse,lack of rich details and texture features in the reconstructed HR images,and excessive time consumption for model sampling.To address these problems,this paper proposes a Latent Feature-oriented Diffusion Probability Model(LDDPM).First,we designed a conditional encoder capable of effectively encoding LR images,reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed images.We then employed a normalized flow and multimodal adversarial training,learning from complex multimodal distributions,to model the denoising distribution.Doing so boosts the generative modeling capabilities within a minimal number of sampling steps.Experimental comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics,providing a fresh perspective for tackling SISR tasks.
