Time series simulation by conditional generative adversarial net 3390/rs14010144. Aug 1, 2022 · Generative adversarial networks (GANs) are a powerful class of generative models, and can generate realistic scenarios for renewable power sources without the need for any modeling assumptions. May 11, 2023 · To address these problems, we propose a new method called time-series conditional generative adversarial network with improved Hausdorff distance (IH-TCGAN). The json representation of the dataset with its distributions based on DCAT. However, this model assumes a unimodal distribution and Jan 1, 2019 · Quickly grasp key insights from "time-series-simulation-by-conditional-generative-adversarial-net", published in SSRN Electronic Journal. Extensive testing of the generative model is performed Furthermore, long time-series data streams hugely increase the dimension of the target space, which may render generative modeling infeasible. Our approach re-lies on Generative Adversarial Networks (GANs) [23] Lithium-ion Battery Capacity Prediction via Conditional Recurrent Generative Adversarial Network-based Time-Series Regeneration Myisha A. This mechanism has been termed as Time-series Generative Adversarial Network or TimeGAN. The conditions include both categorical and continuous variables with different auxiliary information. D. Nov 23, 2021 · The prime contribution of this work are:- Generate realistic time series data using GRU-based conditional generative adversarial networks. To this end, we introduce a novel economics-driven loss function for the generator. We propose a novel framework for generating realistic time-series data that combines the flexibility of the unsupervised paradigm with the control afforded by supervised training. , Shi, G. Jan 1, 2019 · We propose a methodology to approximate conditional distributions in the elliptope of correlation matrices based on conditional generative adversarial networks. We employ a conditional GAN framework to train our model with adversarial training. 2019. To overcome these challenges, motivated by the autoregressive models in econometric, we are interested in the conditional distribution of future time series given the past information. rer. Jun 30, 2020 · Modeling synthetic data using a Generative Adversarial Network (GAN) has been at the heart of providing a viable solution. Our work focuses on one dimensional times series and explores the few shot approach, which is the ability of an algorithm to perform well with limited data. Specifically, the output of quantile regression networks is ex-panded from a set of fixed quantiles to the whole Quantile Function by a univariate mapping from a latent uniform distribution to the target Mar 1, 2024 · Accordingly, we introduce the conditional generative adversarial network (cGAN) [30]. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN Apr 25, 2019 · Generative Adversarial Net (GAN) has been proven to be a powerful machine learning tool in image data analysis and generation. 11419. The conditions can be both categorical and continuous They show that the conditional GAN (CGAN) model demonstrates robust and satisfactory performance in generating high-dimensional time series by various testing sets, including using the data of the COVID-19 period as the test set. GANs have been gaining a lot of traction within the deep learning research community since their inception in 2014 [38 Dec 1, 2019 · Existing methods that bring generative adversarial networks (GANs) into the sequential setting do not adequately attend to the temporal correlations unique to time-series data. Oct 24, 2025 · Conditional Generative Adversarial Networks (CGANs) are a specialized type of Generative Adversarial Network (GAN) that generate data based on specific conditions such as labels or descriptions. Conditional generative adversarial networks are used to learn the patterns of a real dataset of hourly-sampled week-long load profiles and generate unique synthetic profiles on demand, based on the season and type of load required. Mar 23, 2023 · Considering the advantage of the generative adversarial network (GAN) in capturing realistic data distribution, this paper applies conditional GAN (CGAN) to car-following modeling. [20] Alireza Kooch Jul 15, 2025 · The model integrates a response spectrum discriminator (RS-D) and conditional variables, including moment magnitude, rupture distance, fault type, and time-averaged shear-wave velocity at the top 30 m, to ensure spectrum compatibility and physical consistency in the generated acceleration time histories. Evaluate the performance of different time series forecasting models using generated data as training data and real data as testing data. Our simulation studies show that Generative Adversarial Net (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. However, this model assumes a unimodal distribution and tries to Sep 2, 2020 · This paper proposes to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data and provides an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation. dsxbj nsbvit wrgxhub reatg jpqvsrg utobubb bqqvu dbl rndxjb tlzja ojmk qqqgy gbvhwes afltfs rzby