Generating Realistic Stock Market Order Streams

We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks (GANs). Our Stock-GAN model employs a conditional Wasserstein GAN to capture history dependence of orders. The generator design includes specially crafted aspects including components that approximate the market's auction mechanism, augmenting the order history with order-book constructions to improve the generation task. We perform an ablation study to verify the usefulness of aspects of our network structure. We provide a mathematical characterization of distribution learned by the generator. We also propose statistics to measure the quality of generated orders. We test our approach with synthetic and actual market data, compare to many baseline generative models, and find the generated data to be close to real data.

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A Two-Stage GAN for High-Resolution Retinal Image Generation and Segmentation

Electronics ◽

10.3390/electronics11010060 ◽

2021 ◽

Vol 11 (1) ◽

pp. 60

Author(s):

Paolo Andreini ◽

Giorgio Ciano ◽

Simone Bonechi ◽

Caterina Graziani ◽

Veronica Lachi ◽

...

Keyword(s):

High Resolution ◽

Retinal Vessel ◽

Real Data ◽

Generative Adversarial Networks ◽

Retinal Images ◽

Two Stage ◽

Generation Task ◽

Training Samples ◽

Vessel Structure ◽

Semantic Label

In this paper, we use Generative Adversarial Networks (GANs) to synthesize high-quality retinal images along with the corresponding semantic label-maps, instead of real images during training of a segmentation network. Different from other previous proposals, we employ a two-step approach: first, a progressively growing GAN is trained to generate the semantic label-maps, which describes the blood vessel structure (i.e., the vasculature); second, an image-to-image translation approach is used to obtain realistic retinal images from the generated vasculature. The adoption of a two-stage process simplifies the generation task, so that the network training requires fewer images with consequent lower memory usage. Moreover, learning is effective, and with only a handful of training samples, our approach generates realistic high-resolution images, which can be successfully used to enlarge small available datasets. Comparable results were obtained by employing only synthetic images in place of real data during training. The practical viability of the proposed approach was demonstrated on two well-established benchmark sets for retinal vessel segmentation—both containing a very small number of training samples—obtaining better performance with respect to state-of-the-art techniques.

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Generative adversarial networks for generating synthetic features for Wi-Fi signal quality

PLoS ONE ◽

10.1371/journal.pone.0260308 ◽

2021 ◽

Vol 16 (11) ◽

pp. e0260308

Author(s):

Mauro Castelli ◽

Luca Manzoni ◽

Tatiane Espindola ◽

Aleš Popovič ◽

Andrea De Lorenzo

Keyword(s):

Synthetic Data ◽

Real Data ◽

Generative Models ◽

Generative Adversarial Networks ◽

Signal Quality ◽

Quality Service ◽

High Quality ◽

The Real ◽

Adversarial Networks ◽

High Quality Service

Wireless networks are among the fundamental technologies used to connect people. Considering the constant advancements in the field, telecommunication operators must guarantee a high-quality service to keep their customer portfolio. To ensure this high-quality service, it is common to establish partnerships with specialized technology companies that deliver software services in order to monitor the networks and identify faults and respective solutions. A common barrier faced by these specialized companies is the lack of data to develop and test their products. This paper investigates the use of generative adversarial networks (GANs), which are state-of-the-art generative models, for generating synthetic telecommunication data related to Wi-Fi signal quality. We developed, trained, and compared two of the most used GAN architectures: the Vanilla GAN and the Wasserstein GAN (WGAN). Both models presented satisfactory results and were able to generate synthetic data similar to the real ones. In particular, the distribution of the synthetic data overlaps the distribution of the real data for all of the considered features. Moreover, the considered generative models can reproduce the same associations observed for the synthetic features. We chose the WGAN as the final model, but both models are suitable for addressing the problem at hand.

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Generating sequential electronic health records using dual adversarial autoencoder

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocaa119 ◽

2020 ◽

Vol 27 (9) ◽

pp. 1411-1419 ◽

Cited By ~ 1

Author(s):

Dongha Lee ◽

Hwanjo Yu ◽

Xiaoqian Jiang ◽

Deevakar Rogith ◽

Meghana Gudala ◽

...

Keyword(s):

Electronic Health Records ◽

Predictive Modeling ◽

Medical Information ◽

Real Data ◽

Generative Models ◽

Generative Adversarial Networks ◽

Health Records ◽

Clinical Databases ◽

Electronic Health ◽

Clinical Records

Abstract Objective Recent studies on electronic health records (EHRs) started to learn deep generative models and synthesize a huge amount of realistic records, in order to address significant privacy issues surrounding the EHR. However, most of them only focus on structured records about patients’ independent visits, rather than on chronological clinical records. In this article, we aim to learn and synthesize realistic sequences of EHRs based on the generative autoencoder. Materials and Methods We propose a dual adversarial autoencoder (DAAE), which learns set-valued sequences of medical entities, by combining a recurrent autoencoder with 2 generative adversarial networks (GANs). DAAE improves the mode coverage and quality of generated sequences by adversarially learning both the continuous latent distribution and the discrete data distribution. Using the MIMIC-III (Medical Information Mart for Intensive Care-III) and UT Physicians clinical databases, we evaluated the performances of DAAE in terms of predictive modeling, plausibility, and privacy preservation. Results Our generated sequences of EHRs showed the comparable performances to real data for a predictive modeling task, and achieved the best score in plausibility evaluation conducted by medical experts among all baseline models. In addition, differentially private optimization of our model enables to generate synthetic sequences without increasing the privacy leakage of patients’ data. Conclusions DAAE can effectively synthesize sequential EHRs by addressing its main challenges: the synthetic records should be realistic enough not to be distinguished from the real records, and they should cover all the training patients to reproduce the performance of specific downstream tasks.

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Generative models for fast cluster simulations in the TPC for the ALICE experiment

EPJ Web of Conferences ◽

10.1051/epjconf/201921406003 ◽

2019 ◽

Vol 214 ◽

pp. 06003 ◽

Cited By ~ 3

Author(s):

Kamil Deja ◽

Tomasz Trzcin´ski ◽

Łukasz Graczykowski

Keyword(s):

Computational Cost ◽

Real Life ◽

Synthetic Data ◽

Real Data ◽

Generative Models ◽

Generative Adversarial Networks ◽

Detector Response ◽

Alice Experiment ◽

The Real ◽

Speed Up

Simulating the detector response is a key component of every highenergy physics experiment. The methods used currently for this purpose provide high-fidelity results. However, this precision comes at a price of a high computational cost. In this work, we introduce our research aiming at fast generation of the possible responses of detector clusters to particle collisions. We present the results for the real-life example of the Time Projection Chamber in the ALICE experiment at CERN. The essential component of our solution is a generative model that allows to simulate synthetic data points that bear high similarity to the real data. Leveraging recent advancements in machine learning, we propose to use conditional Generative Adversarial Networks. In this work we present a method to simulate data samples possible to record in the detector based on the initial information about particles. We propose and evaluate several models based on convolutional or recursive networks. The main advantage offered by the proposed method is a significant speed-up in the execution time, reaching up to the factor of 102 with respect to the currently used simulation tool. Nevertheless, this speed-up comes at a price of a lower simulation quality. In this work we adapt available methods and show their quantitative and qualitative limitations.

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A Non-Parametric Generative Model for Human Trajectories

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/530 ◽

2018 ◽

Cited By ~ 6

Author(s):

Kun Ouyang ◽

Reza Shokri ◽

David S. Rosenblum ◽

Wenzhuo Yang

Keyword(s):

Human Mobility ◽

Real Data ◽

Generative Models ◽

Generative Model ◽

Generative Adversarial Networks ◽

Semantic Features ◽

Location Data ◽

Adversarial Networks ◽

Data Points ◽

Non Parametric

Modeling human mobility and synthesizing realistic trajectories play a fundamental role in urban planning and privacy-preserving location data analysis. Due to its high dimensionality and also the diversity of its applications, existing trajectory generative models do not preserve the geometric (and more importantly) semantic features of human mobility, especially for longer trajectories. In this paper, we propose and evaluate a novel non-parametric generative model for location trajectories that tries to capture the statistical features of human mobility {\em as a whole}. This is in contrast with existing models that generate trajectories in a sequential manner. We design a new representation of locations, and use generative adversarial networks to produce data points in that representation space which will be then transformed to a time-series location trajectory form. We evaluate our method on realistic location trajectories and compare our synthetic traces with multiple existing methods on how they preserve geographic and semantic features of real traces at both aggregated and individual levels. The empirical results prove the capability of our model in preserving the utility of real data.

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Investigation of Timing Anomalies in the Russian Stock Market in the Post-Crisis Period

Economics taxes & law ◽

10.26794/1999-849x-2019-12-3-37-47 ◽

2019 ◽

Vol 12 (3) ◽

pp. 37-47

Author(s):

I. Ya. Lukasevich

Keyword(s):

Stock Market ◽

Information Technologies ◽

Real Data ◽

Weak Form ◽

Fair Value ◽

Presidential Decree ◽

Day Of The Week ◽

Russian Stock Market ◽

Economic Growth Rates ◽

Global And Local

The implementation of the May presidential decree aimed at Russia’s joining the top five global economies and achieving economic growth rates above the world’s average while maintaining macroeconomic stability requires a highly developed and efficient stock market ensuring the accumulation of capital and its deployment in the most promising and productive sectors of the economy.The subject of the research is timing anomalies in the Russian stock market in 2012–2018. The relevance of the research is due to the information inefficiency of the Russian stock market and its imperfections leading to significant price deviations from the «fair» value of assets and depriving investors of the opportunity to form various strategies for deriving additional revenues not related to fundamental economic factors and objective processes occurring in the global and local economies and the economy of an individual business entity. Based on the trend analysis of the Broad Market USD Index (RUBMI), the paper demonstrates a methodology for simulating the analysis of price anomalies on large arrays of real data using statistical data processing methods and modern information technologies. The paper concludes that though the Russian stock market lacks even the weak form of efficiency, such well-known timing anomalies as the “day-of-the-week” effect and the “month” effect have not been observed in the recent years. Therefore, investors could not use these anomalies to derive regular revenues above the market average.

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Generative Adversarial Networks (GANs)

ACM Computing Surveys ◽

10.1145/3446374 ◽

2021 ◽

Vol 54 (3) ◽

pp. 1-42

Author(s):

Divya Saxena ◽

Jiannong Cao

Keyword(s):

Optimization Technique ◽

Generative Models ◽

Generative Adversarial Networks ◽

Network Architectures ◽

Research Directions ◽

Research Issues ◽

Design And Optimization ◽

Adversarial Networks ◽

Comprehensive Survey ◽

Selection Of

Generative Adversarial Networks (GANs) is a novel class of deep generative models that has recently gained significant attention. GANs learn complex and high-dimensional distributions implicitly over images, audio, and data. However, there exist major challenges in training of GANs, i.e., mode collapse, non-convergence, and instability, due to inappropriate design of network architectre, use of objective function, and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions, and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on the broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present promising research directions in this rapidly growing field.

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