Gan time series prediction. This script is used for numerical values prediction.

Gan time series prediction. Generative adversarial networks (GAN) in a reduced-order model (ROM) framework for time series prediction, data assimilation and uncertainty quantification - viluiz/gan In this proposal, it’s going to be studied the integration of a BiLSTM layer with a time series obtained by GAN in order to improve the forecasting of all the features provided by the dataset in terms of accuracy and, consequently, improving behaviour prediction. stock forecasting with sentiment variables (with lstm as generator and mlp as discriminator) tensorflow: gan code without sentiment variables (1. Inspired by the success of Generative Adversarial Networks (GAN) in image generation, we propose to learn the overall distribution of a multivariate time series dataset with GAN, which is further used to generate the missing values for each sample. Financial time series prediction is challenging due to the uncertainty of financial markets, especially in China’s stock market. This newly designed loss function renders In this article, we review GAN variants designed for time series related applications. May 27, 2021 · The study aims at generating initial and directional insights in the applicability of conditional recurrent generative adversarial nets for the imputation and forecasting of medical time series Tanya Juneja, Shalini Bhaskar Bajaj, and Nishu Sethi Abstract Synthetic time series data generation is a wide area to research, and lot of attention has drawn recently. A model that generates synthetic time-series Aug 3, 2024 · The integration of time-series GAN-generated synthetic flood events with real data improved the robustness and accuracy of the autoencoder model, enabling more reliable predictions of extreme flood events. Apr 15, 2024 · This paper tackles the challenge of time series forecasting in the presence of missing data. Financial time-series prediction is no exception. The framework utilizes a Conditional GAN (C-GAN) to realistically impute missing values in Jan 11, 2024 · However, the construction of accurate time series prediction models hinges upon the availability of complete and high-quality datasets [3]. Extreme gradient boosting (XGBoost) is used for feature extraction to improve prediction accuracy. In this article, we explored how GANs Apr 18, 2023 · Inspired by generative adversarial networks (GANs), we propose TS-GAN, a Time-series GAN architecture based on long short-term memory (LSTM) networks for sensor-based health data augmentation, thereby improving the performance of deep learning-based classification models. GAN: Time Series Generation Package This package provides an implementation of Generative Adversarial Networks (GANs) for time series generation, with flexible architecture options. We demonstrate the utility and performance of the proposed method through two examples. In this paper, a new multivariate time series prediction method is proposed. May 23, 2025 · Anomaly detection in time series data-identifying points that deviate from expected patterns-is a common challenge across various domains, including manufacturing, medical imaging, and cybersecurity. Jul 1, 2021 · In general, we are facing a challenge: the task of using observed time series in the past to predict the unknown time series in long-term prediction– the larger the prediction steps are, the harder the problem is. After training, the GAN can be 1 Introduction Stock price prediction in capital markets has been consistently researched using deep learning, just last year, there were at least 9700 papers written on the subject according Google Scholar. However, there are no existing generative models that show good performance for both types without any model changes. The title of this repo is TimeSeries-GAN or TSGAN, because it is generating realistic synthetic time series data from a sampled noise data for biological purposes. GAN is widely used in image generating, but not in time series prediction. Prior attempts at generating time-series data like the recurrent (conditional) GAN relied on recurrent neural networks (RNN, see Chapter 19, RNN for Multivariate Time Series and Sentiment Analysis) in the roles of generator and discriminator. The framework adopts a multi-Wasserstein loss on decision-related quantities and an overlapped block-sampling approach for sample efficiency. Alibaba stock is taken as the research object, using XGBoost to optimize Jan 18, 2024 · Stock price prediction is a significant field of finance research for both academics and practitioners. , 2020). Though the emergence of Generative Adversarial Networks (GANs) and Graph Convolution Networks (GCNs) provides more possibilities to improve imputation performance, how to achieve the optimal latent code and precisely model the properties of incomplete time series remain a challenge. Generating synthetic data that is physically plausible is a promising way to tackle this challenge. (ii) a transformer based predictor, which makes long-range predictions using both generated and observed data. Due to its great potential, it has been used in many different contexts. Aug 31, 2022 · In this article, we review GAN variants designed for time series related applications. This study proposes a new unsupervised multivariate time series anomaly prediction model called the Predictive Wasserstein Generative Adversarial Network with Gradient Penalty (PW-GAN-GP). This paper introduces a generative adversarial network model that incorporates an attention mechanism (GAN-LSTM-Attention) to improve the accuracy of stock price prediction. We consider limitations posed specifically on time-series data and present a model that can generate synthetic time-series which can be used in place of real data. In real Jul 13, 2023 · Fin-GAN: Forecasting and Classifying Financial Time Series via Generative Adversarial Networks Milena Vuletić, Mihai Cucuringu and Felix Prenzel We investigate the use of Generative Adversarial Networks (GANs) for probabilistic forecasting of financial time series. May 28, 2021 · We propose a new method in which a generative adversarial network (GAN) is used to quantify the uncertainty of forward simulations in the presence of observed data. Click To Get Model/Code. This script is used for numerical values prediction. Numerous studies have proved that the stock movement can be fully reflect various internal features of stock price including non-stationary behavior, high persistence in the conditional variance. Feature Extraction is performed and ARIMA and Fourier series models are made. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator Sep 5, 2024 · Abstract Multivariate time series have more complex and high-dimensional characteristics, which makes it difficult to analyze and predict the data accurately. The generator of MTGAN uses a gated recurrent unit (GRU) with a smooth conditional matrix to generate sequences and uncommon diseases. Traditional anomaly detection algorithms can achieve the detection of shallow level anomalies when facing such data, however To remedy the challenges, we propose a novel architecture called Time Series GAN (TSGAN). Various papers have proposed different techniques in stock market forecasting, but no model can provide accurate predictions. Mar 10, 2025 · To overcome this issue, we proposed a modified Conditional Wasserstein Generative Adversarial Network with a Gradient Penalty (CWGAN-GP) for generating synthetic time-series data according to the original data distribution. The data with time series features often has non-stationary properties, and its fluctuation amplitude changes with time. According to the previous study, the GAN-based AD outperformed the cumulative sum (CUSUM) chart. See full list on github. Jul 3, 2020 · Time series anomaly detection is widely used to monitor the equipment sates through the data collected in the form of time series. Using deep learning algorithms to establish prediction models for sensor data is an effective approach; however, the performance of these models is significantly influenced by the quantity and quality of the training data. Sep 1, 2023 · Time series imputation is essential for real-world applications. Jun 15, 2024 · GAN avoids the difficulty of directly solving the maximum likelihood function by alternating the training of two deep neural networks, the generator, and the discriminator [20]. At the same time, supervised models for sequence prediction—which allow finer control over network dynamics—are inherently deterministic. GAN have been increasingly researched for data generation, anomaly detection, time-series prediction, and classification as researchers continue to invest in them. Abstract—Considering the problems of the model collapse and the low forecast precision in predicting the financial time series of the generative adversarial networks (GAN), we apply the WGAN-GP model to solve the gradient collapse. Previously, a method has been developed which enables GANs to make time series predictions and data assimilation by training Feb 6, 2022 · To tackle these problems, we introduce TTS-GAN, a transformer-based GAN which can successfully generate realistic synthetic time-series data sequences of arbitrary length, similar to the real ones. Jul 15, 2023 · To address the challenges discussed above, this paper proposes a new unsupervised multivariate time series anomaly prediction model, the Predictive Wasserstein Generative Adversarial Network with Gradient Penalty (PW-GAN-GP), which is a variant of GAN. Hence, the volatility trend prediction in financial time series (FTS) has been an active topic for several decades. We propose a new method in which a generative adversarial network (GAN) is used to quantify the uncertainty of forward simulations in the presence of observed data. The data was Oct 28, 2024 · Current Generative Adversarial Network (GAN)-based approaches for time series generation face challenges such as suboptimal convergence, information loss in embedding spaces, and instability. Mar 23, 2023 · Deep learning has achieved tremendous success in various applications owing to its robust feature representations of complex high-dimensional nonlinear data. Alibaba stock is taken as the research object, using XGBoost to Dec 15, 2022 · The proposed GAN model is TGAN-Modified-WGAN-GP, a tabular generative adversarial networks (TGAN) architecture for training with TGAN to produce synthetic time-series data that evolved from a modified version of Wasserstein GAN with gradient penalty (WGAN-GP), which improved training stability, made it more robust to mode collapse problems, and made convergence faster. This article has covered fundamental concepts, practical steps, and the necessary tools to perform time series forecasting using Python. e. Nov 15, 2023 · To solve the few-shot learning problem in NWFs, a novel multi-gradient evolutionary deep learning neural network (EATDLNN) prediction model is proposed, which incorporates the time-series GAN (TimeGAN) and multivariate variational mode decomposition (MVMD) method. We will continue to update Considering the problems of the model collapse and the low forecast precision in predicting the financial time series of the generative adversarial networks (GAN), we apply the WGAN-GP model to solve the gradient collapse. Feb 1, 2024 · DLinear (Zeng, Chen, Zhang, & Xu, 2023): DLinear decomposes the time series into a trend series and a remainder series, and then uses two single-layer linear networks to model these two series for prediction tasks. In this section, we additionally discuss methods related on the periphery, including RNN-based sequence Jul 18, 2021 · The stock market has been a popular topic of interest in the recent past. We characterize the generalization properties of DAT-CGAN and Sep 8, 2021 · This implementation can be found here. Sep 11, 2024 · Generative Adversarial Networks offer a fresh and powerful approach to forecasting, with the potential to handle complex, non-linear patterns in time series data, such as climate data. Mar 23, 2022 · We propose an improved GAN model for time series nephogram prediction in the following 3 aspects. Inspired by generative adversarial networks (GAN), which have been Jun 30, 2020 · It is abundantly clear that time dependent data is a vital source of information in the world. In order to cope with the above challenge, we propose VAECGAN (Variational Auto-Encoder Conditional Generative Adversarial Network). In this study, a novel hybrid framework combining the Mar 10, 2025 · research-article GAN-based synthetic time-series data generation for improving prediction of demand for electric vehicles Authors: Subhajit Chatterjee , Debapriya Hazra , Yung-Cheol Byun Jul 15, 2023 · This study proposes a new unsupervised multivariate time series anomaly prediction model called the Predictive Wasserstein Generative Adversarial Network with Gradient Penalty (PW-GAN-GP). This article will guide you In this paper we introduce TimeGAN, a novel framework for time-series generation that combines the versatility of the unsupervised GAN approach with the control over conditional temporal dynamics afforded by supervised autoregressive models. The growth in the inflation rate has compelled people to invest in the stock and commodity markets and other areas rather than saving. The dual architecture of GANs, comprising a Generator and a Discriminator 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. TSGAN uses two GANs in unison to model fake time series examples. However, no framework for comparison is provided in their works. The prediction model dataset in this study used nine-axis sensor data (accelerometer, gyroscope, and geomagnetic sensors). First, we Oct 7, 2022 · We propose a new method in which a generative adversarial network (GAN) is used to quantify the uncertainty of forward simulations in the presence of observed data. Similarly, since 2014 To tackle these problems, we introduce TTS-GAN, a transformer-based GAN which can successfully generate realistic synthetic time series data sequences of arbitrary length, similar to the original ones. While the generator performed well on its own in our experiment, the current version does not achieve performance Nov 25, 2023 · We introduce the decision-aware time-series conditional generative adversarial network (DAT-CGAN), a method for the generation of time-series data that is designed to support decision-making. In this study, we show how to accurately anticipate stock prices using a prediction model based on the Generative Adversarial Networks (GAN) method. The assumption for generating multivariate time sequenced data is well proportioned and continuous without missing values. This was verified during multiple training runs As such, our approach straddles the intersection of multiple strands of research, combining themes from autoregressive models for sequence prediction, GAN-based methods for sequence generation, and time-series representation learning. Dec 8, 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. A productive time series data model should retain momentum, such that the new sequence maintains the Apr 24, 2022 · Stock market predictions help investors benefit in the financial markets. We propose a novel framework that combines the strengths of Generative Adversarial Networks (GANs) and Bayesian inference. In this paper, we propose a Graph-Attention-based Generative Adversarial Network (GAT-GAN) that explicitly includes In this paper, we utilize the Sentiment Analysis technique in NLP field to provide the opinion influence factors for the algorithmic model by performing sentiment analysis on the text of the research report, introduce the GAN algorithm which is more compatible with the logic of the temporal data operation, and on the basis of which we add the Text Pathway, and put forward the text-assisted Aug 22, 2024 · Training a GAN model for time series data prediction requires careful tuning of hyperparameters and attention to convergence issues. The original GAN was extended to show that minimizing the Wasserstein Oct 15, 2023 · The Transformer, commonly employed for time series prediction, is selected as the generative model G to predict the output Y T + 1 based on the input sequence data X, since the SOC estimation is a typical times series issue, i. 5 year-long dataset) keras: gan code with sentiment variables (3-month-long dataset) stock (AAPL) prediction for the open price the next day with the past five days' prices utilized MAPE as the metric to evaluate the training results Jan 18, 2023 · Abstract We investigate the use of Generative Adversarial Networks (GANs) for probabilistic forecasting of financial time series. We proposed a generative adversarial network (GAN) model for time series satellite cloud image prediction in this research. Therefore, this paper summarizes the current work of time-series signals generation based on GAN and the existing evaluation methods of GAN. Technical analysis on the stock market with the help of technical Jan 10, 2023 · In this paper, we proposed a GAN and informer-based model called imputeGAN for solving missing multidimensional time series data. Taking time series information as Aug 1, 2023 · Nonetheless, to our best knowledge, it remains unclear how effectively GANs can serve as a general-purpose solution to learn representations for time series recognition, i. We aimed to generate complex time series multi-channel ion channel data because these synthetic data then can be used to May 1, 2022 · Besides sequence-to-sequence models based on recurrent neural networks (RNN) or transformers, generative adversarial networks (GAN) have been suggested to compute such infills or predictions. The fusion of time-series prediction model such as Auto-Regressive Integrated Moving Average May 10, 2023 · A sufficient amount of data is crucial for high-performance and accurate trend prediction. However, traditional GANs often struggle to capture complex relationships between features which results in generation of unrealistic multivariate time-series data. Modeling synthetic data using a Generative Adversarial Network (GAN) has been at the heart of providing a viable solution. This newly designed loss function renders GANs more suitable for a classification task, and places them into a supervised learning setting, whilst producing full conditional Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019 - jsyoon0823/TimeGAN Mar 10, 2025 · GAN-based synthetic time-series data generation for improving prediction of demand for electric vehicles Subhajit Chatterjee a , Debapriya Hazra a , Yung-Cheol Byun b Show more Add to Mendeley Nov 2, 2022 · Predicting the shape evolution and movement of remote sensing satellite cloud images is a difficult task requiring the effective monitoring and rapid prediction of thunderstorms, gales, rainstorms, and other disastrous weather conditions. Since there are few studies on time series prediction using GAN, their conclusions are inconsistent according to their studies. Several generative adversarial network (GAN) based methods have been proposed to tackle the problem usually with the assumption that the targeted time series data are well-formatted and complete. Aug 15, 2023 · Limited availability of representative time-to-failure (TTF) trajectories either limits the performance of deep learning (DL)-based approaches on remaining useful life (RUL) prediction in practice or even precludes their application. Jun 12, 2024 · Generally, GAN architectures are used to enrich the understanding of time series samples, such as data augmentation and data transformation, and after obtaining high-quality virtual data, the problem of low prediction accuracy caused by small sample data is significantly improved. ), which aims to comprehensively and systematically summarize the recent advances to the best of our knowledge. GAN-based synthetic time-series data generation for improving prediction of demand for electric vehicles. Aug 1, 2019 · The GAN model produces a time-series that recovers the statistical properties of financial time-series such as the linear unpredictability, the heavy-tailed price return distribution, volatility clustering, leverage effects, the coarse-fine volatility correlation, and the gain/loss asymmetry. In the GAN, we use the Long Short-Term Memory (LSTM) as generate network and Dec 1, 2024 · A multivariate time series imputation model based on generative adversarial networks and multi-head attention (ATTN-GAN) is proposed in this work to reducing the negative consequence of missing data. So, we conduct new studies crucial for the GAN-based AD methods (the MAD-GAN and the TAnoGAN). Jan 28, 2022 · Besides, the existing evaluation methods cannot evaluate the performance of GAN comprehensively. Some tips for successful training include: Aug 16, 2023 · The GAN model consists of an LSTM as the time series generator and an ANN as the discriminator, using the simple moving average and exponentially weighted moving average results as input features for the GAN network, followed by the Fourier transform, ARIMA to create the input features, and finally XGBoost to filter the final prediction data. Jun 8, 2024 · Traditional methods like ARIMA and LSTM have been widely used, but Generative Adversarial Networks (GANs) offer a novel approach with potentially superior performance. At the same time, supervised models for sequence prediction - which allow finer control over network dynamics - are inherently deterministic. , (30) Y T + 1 = G (X) Jan 31, 2024 · Apart from the standard ForGAN (GAN with the ForGAN architecture trained via the BCE loss), we compare our Fin-GAN model with more standard supervised learning approaches to time series forecasting: ARIMA and LSTM. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. Time series data generation has drawn increasing attention in recent years. Jun 3, 2023 · Generative Adversarial Networks (GANs) have proven to be a powerful tool for generating realistic synthetic data. Jan 14, 2025 · In this proposal, it’s going to be studied the integration of a BiLSTM layer with a time series obtained by GAN in order to improve the forecasting of all the features provided by the dataset in terms of accuracy and, consequently, improving behaviour prediction. However, performance deterioration and data bias problems occur in behavioral prediction. GDPR). Dataset and imports The data used in this notebook was downloaded from Yahoo finance and includes: 6 variables - Open, High, Low, Close, Adj Close, Volume 1022 events registered between the period of 1 January 2017 - 24 January 2021. Nov 18, 2024 · In actual production processes, analysis and prediction tasks commonly rely on large amounts of time-series data. However, this method needs to find the best mapping from real-time space to the latent space at the anomaly detection stage, which Oct 1, 2022 · GAN uses available time series degradation data to generate synthetic degradation data that enhances the predictor’s learning and forecast performance, thus improving the RUL prediction accuracy. The present review summarises the current state of published research with regard to GANs utilised for forecasting or imputing time series data. 4. The above considerations inspire us to introduce a Time-series Convolutional GAN (TCGAN). . ATTN-GAN can capture the temporal and spatial correlation of time series, and has a good capacity to learn data distribution. g. Taking time series information as May 23, 2022 · Many real-world tasks are plagued by limitations on data: in some instances very little data is available and in others, data is protected by privacy enforcing regulations (e. Further, the ability of Deep Learning models to make predictions on the time series data has been proven time and again. Keywords: Digital Twin, GAN, BiLSTM Jul 23, 2021 · In this paper, we review GAN variants designed for time series related applications. By generating synthetic time series data and refining models through adversarial training, GANs might be able to enhance prediction accuracy and better generalize over time series patterns. To address the challenges discussed above, this paper proposes a new unsupervised multivariate time series anomaly prediction model, the Predictive Wasserstein Genera-tive Adversarial Network with Gradient Penalty (PW-GAN-GP), which is a variant of GAN. In this study we use a time-series generative adversarial network (TimeGAN) to synthesize multivariate agricultural Jul 3, 2020 · Precision, Recall and F1 score of representative time series anomaly detection methods based on sample reconstruction and our method LSTM-based VAE-GAN. The example is next two-hour traffic speed prediction based on historical speeds. In this article, we review GAN variants designed for time series related applications. To overcome these challenges, we introduce an advanced framework that integrates the advantages of an autoencoder-generated embedding space with the adversarial training dynamics of GANs. This study proposed that researchers conduct behavioral prediction using text-to-numeric generative adversarial network (TN-GAN)-based multidimensional time-series Apr 21, 2023 · This study proposed that researchers conduct behavioral prediction using text-to-numeric generative adversarial network (TN-GAN)-based multidimensional time-series augmentation to minimize the data bias problem. To address this challenge, this paper proposes a dual-layer transfer model based on Generative Adversarial Networks (GANs) aiming to Predicting the shape evolution and movement of remote sensing satellite cloud images is a difficult task requiring the effective monitoring and rapid prediction of thunderstorms, gales, rainstorms, and other disastrous weather conditions. At present, the deep learning method based on generative adversarial networks (GAN) has emerged for time series anomaly detection. The obtained results demonstrate improved prediction performance. For this purpose, we combine multi-attention based RNN and GAN to achieve better long-term prediction results. Regrettably, missing data poses a common challenge in time series modeling, arising from factors such as equipment failures during data acquisition and transmission errors [4]. May 27, 2021 · The study aims at generating initial and directional insights in the applicability of conditional recurrent generative adversarial nets for the imputation and forecasting of medical time series data. Both the generator and discriminator networks of the GAN model are built using a pure transformer encoder architecture. The model was compared with traditional complementary methods and models used to predict time series data. Abstract Current Generative Adversarial Network (GAN)-based approaches for time series generation face challenges such as suboptimal convergence, information loss in embedding spaces, and instability. Jul 26, 2025 · The application of Generative Adversarial Networks (GANs) has revolutionized time series analysis, enabling tasks such as data synthesis, imputation, forecasting, and anomaly detection. This method was applied to degradation modeling and lifespan prediction of various critical components. Feb 2, 2023 · In this article, we review GAN variants designed for time series related applications. Jan 29, 2023 · Behavioral prediction modeling applies statistical techniques for classifying, recognizing, and predicting behavior using various data. However, it is difficult and time-consuming to collect agricultural data over long periods of time; the consequence of such difficulty is datasets that are characterized by missing data. Time series forecasting uses previous stock price patterns to anticipate future prices. 1 Introduction Stock price prediction in capital markets has been consistently researched using deep learning, just last year, there were at least 9700 papers written on the subject according Google Scholar. This method Jan 8, 2025 · Therefore, this paper proposes an improved temporal convolutional network (TCN) time-series generative adversarial network (GAN) with an attention mechanism, called the Attention–TCN–TimeGAN, to enhance InSAR surface deformation data for better prediction results. Nov 1, 2024 · Request PDF | On Nov 1, 2024, Subhajit Chatterjee and others published GAN-based synthetic time-series data generation for improving prediction of demand for electric vehicles | Find, read and A curated list of Diffusion Models for Time Series, SpatioTemporal Data and Tabular Data with awesome resources (paper, code, application, review, survey, etc. This paper presents a stock prediction model with a methodology that uses a Generative Adversarial Network (GAN) with Gated Recurrent Units (GRU) as a generator to generate a future stock price based on historical By understanding the characteristics of time series data, preparing your data effectively, and employing various forecasting techniques, you can create robust models that deliver accurate predictions. Recently, Generative Adversarial Networks (GANs) have shown great promise in improving anomaly detection performance. This paper analyzes modifications to GAN architectures specifically designed for Jan 16, 2025 · Predicting the time series energy consumption data of manufacturing processes can optimize energy management efficiency and reduce maintenance costs for enterprises. This paper aims to use GAN to predict the stock price and check whether the adversarial system can help improve the time series prediction. We propose a taxonomy of discrete-variant GANs and continuous-variant GANs, in which GANs deal with discrete time series and continuous time series data. , classification and clustering. com Jan 27, 2021 · Jinsung Yoon and Daniel Jarret have proposed, in 2019, a novel GAN architecture to model sequential data – TimeGAN — that I’ll be covering with a practical example throughout this blog post. Then, transforming one-dimensional time series into two-dimensional images by GRP makes full use of the global and local information of time series. For instance, it is extensively employed for predicting time series data. 2. With the training time and computational power that was within our reach, it seems like our Generator tended strongly to learning one specific simple curve, often shaped like a hook, a right angle or a straight line. Expert Systems with Applications, 264, Article 125838. Time Series Forecasting analyses and predicts time-ordered data. Jun 1, 2025 · In the proposed method we introduce the integration of a Bidirectional LSTM (BiLSTM) layer with a time series obtained by GAN that leads to improved forecasting of all feature of the available dataset in terms of accuracy. Anomaly detection (AD) for times series data using the generative adversarial network (GAN) has been proposed in recent years. Jun 1, 2024 · To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network, we propose a convolutional neural network (CNN)-long short-term memory (LSTM) prediction model based on the incremental attention mechanism. While GANs are still relatively new in the forecasting world, their ability to generate realistic data offers exciting possibilities for the future of AI-driven predictions. We developed a GAN-based imputation and prediction approach for time series data. Jan 1, 2017 · TimeGAN - Implemented accordingly with the paper This notebook is an example of how TimeGan can be used to generate synthetic time-series data. Related to Time Series, recurring neural networks such as long short-term memory (LSTM) had been successfully tested to replicate stock price distributions. Jan 10, 2025 · In this work, we present a dual-channel Transformer conditional GAN model to generate multivariate time series signals of arbitrary length and class to address the data imbalance problem for RUL prediction. Therefore, we present a general purpose model capable of Aug 30, 2023 · Therefore, this paper proposes a time-series generation and prediction network based on GRU-GAN to solve the problem of sample enhancement prediction under minor degradation conditions. Jan 1, 2024 · Condition monitoring plays a crucial role in real-time evaluation of system states, but requires a large amount of measurement data to develop an accu… Dec 27, 2022 · First, the data is subjected to a series of pre-processing operations, including data smoothing. - CakeBnut1996/GAN-Time-Series Aug 31, 2023 · In this work, we propose a Multi-label Time-series GAN (MTGAN) to generate EHR and simultaneously improve the quality of uncommon disease generation. The GAN network model is generally used for Direct application of GAN architecture on time-series data like C-RNN-GAN or RCGAN [6] try to generate the time-series data recurrently sometimes taking the generated output from the previous step as input (like in case of RCGAN) along with the noise vector. This is possibly due to a number of reasons. Project analyzes Amazon Stock data using Python. The challenge has been for applications in machine learning to gain access to a considerable amount of quality data needed for algorithm development and analysis. In this paper, we propose a new forecasting strategy called Genera-tive Forecasting (GenF), which generates synthetic data for the next few time steps and then makes long-range forecasts based on generated and observed data. In GAN-based methods, an effective latent code Oct 17, 2021 · We implement GenF via three components: (i) a novel conditional Wasserstein Generative Adversarial Network (GAN) based generator for synthetic time series data generation, called CWGAN-TS. After training, the GAN can be used to predict the evolution of the spatial distribution of the simulation states and observed data is assimilated. As such, our approach straddles the intersection of multiple strands of research, combining themes from autoregressive models for sequence prediction, GAN-based methods for sequence generation, and time-series representation learning. It’s based on a paper by the same authors. After training, the GAN can be May 28, 2021 · Previously, a method has been developed which enables GANs to make time series predictions and data assimilation by training a GAN with unconditional simulations of a high-fidelity numerical model. To this end, we introduce a novel economics-driven loss function for the generator. Additional Related Work TimeGAN integrates ideas from autoregressive models for sequence prediction [1, 2, 3], GAN-based methods for sequence generation [4, 5, 6], and time-series representation learning [7, 8, 9]—the relation and details for which are discussed in the main manuscript. Time series data types can be broadly classified into regular or irregular. A recreation of the results of the original Time GAN paper is very hard to achieve. Traditional methods often struggle with such data, which leads to inaccurate predictions. This Aug 1, 2019 · The GAN model produces a time-series that recovers the statistical properties of financial time-series such as the linear unpredictability, the heavy-tailed price return distribution, volatility clustering, leverage effects, the coarse-fine volatility correlation, and the gain/loss asymmetry. GAN for time series prediction, data assimilation and uncertainty quantification. The popular generative model GAN [1], is an unsupervised deep learning method in which two deep networks are pitted against each other to generate synthetic data. This paper innovates to integrate sparse Gaussian Graph Model (GGM) information into Generative Adversarial Network (GAN) to forecast stock price for the Chinese A-share market. Oct 28, 2024 · This approach can capture complex patterns in time series data, making it potentially useful for forecasting. Generating time-series data using TimeGAN TimeGAN (Time-series Generative Adversarial Network) is an implementation for synthetic time-series data. applied GAN to renewable energy scenario generation for the first time, and the results showed that GAN can be quickly extended to generate diverse renewable energy Jan 3, 2025 · Stock price prediction is a typical complex time series prediction problem characterized by dynamics, nonlinearity, and complexity. Finally, the combination of LSTM and improves GAN models for temperature time series prediction. East Carolina University has created The ScholarShip, a digital archive for the scholarly output of the ECU community. We propose a classification of discrete-variant GANs and continuous-variant GANs, in which GANs deal with discrete time series and continuous time series data. Similarly, since 2014 Abstract Time series synthesis is an important research topic in the field of deep learning, which can be used for data augmentation. Generation of Time Series data using generative adversarial networks (GANs) for biological purposes. However, real-world scenarios often face issues such as insufficient or imbalanced data, severely impacting the accuracy of analysis and predictions. stock forecasting with sentiment variables(with lstm as generator and mlp as discriminator) - UalwaysKnow/time-series-prediction-with-gan Time series anomaly detection is an important part of Prognostic and Health Management (PHM), and has been widely studied and followed with interest. The field of deep learning is vast. Previously, a method has been developed which enables GANs to make time series predictions and data assimilation by training a GAN with unconditional simulations of a high-fidelity numerical model. We collect the dataset, preprocess it, extract Mar 10, 2025 · research-article GAN-based synthetic time-series data generation for improving prediction of demand for electric vehicles Authors: Subhajit Chatterjee , Debapriya Hazra , Yung-Cheol Byun Oct 3, 2024 · It can measure the impact of these independent variables on stock prices in stock price prediction. 1 GAN for time series prediction time using a GAN, an algorithm named Predictive GAN (PredGAN) is used here [2 Mar 1, 2025 · A TCN can achieve extensive sequence memory by utilizing dilated convolutions, enabling it to capture long-term dependencies in time-series data, as well as causal convolution, ensuring that the model does not utilize future information when predicting future values, which is particularly crucial for time-series prediction. Our experiment with blood pressure series showed that a generative recurrent autoencoder exhibits si … May 1, 2022 · This paper proposes a novel TSF-CGANs (time series forecasting based on CGANs, TSF-CGANs) algorithm considering conditional generative adversarial networks (CGANs) combined with convolutional neural networks (CNN) and Bi-directional long short-term memory (Bi-LSTM) for improving the accuracy of hourly PV power prediction. Our work Feb 1, 2024 · Although, attention-based RNNs can efficiently represent the dynamic ST relationships between the exogenous (driving) series and target series, they only perform well in one-step and short-term time prediction (Liu et al. Jan 14, 2025 · In this proposal, it is going to be studied the integration of a BiLSTM layer with a time series obtained by GAN in order to improve the forecasting of all the features provided by the dataset in terms of accuracy and, consequently, improving behaviour prediction. rjzd rqkr rkvxs scr zmu biipan wafo ekolo ajfvbj rfgbz