Implementation of Synthesize GAN Model to Detect Outlier in National Stock Exchange Time Series Multivariate Data

This research work explores a novel approach for identifying outliers in stock related time series multivariate datasets, using Generative Adversarial Networks (GANs). The proposed framework harnesses the power of GANs to create synthetic data points that replicate the statistical characteristics of genuine stock related time series. The use of Generative Adversarial Networks to generate tabular data has become more important in a number of industries, including banking, healthcare, and data privacy. The process of synthesizing tabular data with GANs is also provided in this paper. It involves several critical steps, including data collection, preprocessing, and exploration, as well as the design and training using Generator and Discriminator networks. While the discriminator separates genuine samples from synthetic ones, the generator is in charge of producing synthetic data. Generating high quality tabular data with GANs is a complex task, but it has the potential to facilitate data generation in various domains while preserving data privacy and integrity. The results from the experiments confirm that the GAN framework is useful for detecting outliers.  The model demonstrates its proficiency in identifying outliers within stock-related time series data. In comparison, our proposed work also examines the statistics and machine learning models in related application fields

Time Series Synthesis via Multi-scale Patch-based Generation of Wavelet Scalogram

A framework is proposed for the unconditional generation of synthetic time series based on learning from a single sample in low-data regime case. The framework aims at capturing the distribution of patches in wavelet scalogram of time series using single image generative models and producing realistic wavelet coefficients for the generation of synthetic time series. It is demonstrated that the framework is effective with respect to fidelity and diversity for time series with insignificant to no trends. Also, the performance is more promising for generating samples with the same duration (reshuffling) rather than longer ones (retargeting).Comment: 8 pages, 3 figures, 2 table

MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of load profiles in one shot. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads to enable the generation of realistic synthetic load profiles in large quantity for meeting the emerging need in distribution system planning. The novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, it generates a group of load profiles bearing realistic spatial-temporal correlations in one shot. Second, two complementary metrics for evaluating realisticness of generated load profiles are developed: statistics metrics based on domain knowledge and a deep-learning classifier for comparing high-level features. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad-GAN outperforms state-of-the-art approaches in realisticness, computational efficiency, and robustness. With little finetuning, the MultiLoad-GAN approach can be readily extended to generate a group of load or PV profiles for a feeder, a substation, or a service area.Comment: Submitted to IEEE Transactions on Smart Gri

Adversarial Attacks on Deep Neural Networks for Time Series Classification

Time Series Classification (TSC) problems are encountered in many real life data mining tasks ranging from medicine and security to human activity recognition and food safety. With the recent success of deep neural networks in various domains such as computer vision and natural language processing, researchers started adopting these techniques for solving time series data mining problems. However, to the best of our knowledge, no previous work has considered the vulnerability of deep learning models to adversarial time series examples, which could potentially make them unreliable in situations where the decision taken by the classifier is crucial such as in medicine and security. For computer vision problems, such attacks have been shown to be very easy to perform by altering the image and adding an imperceptible amount of noise to trick the network into wrongly classifying the input image. Following this line of work, we propose to leverage existing adversarial attack mechanisms to add a special noise to the input time series in order to decrease the network's confidence when classifying instances at test time. Our results reveal that current state-of-the-art deep learning time series classifiers are vulnerable to adversarial attacks which can have major consequences in multiple domains such as food safety and quality assurance.Comment: Accepted at IJCNN 201

Conditional Generative Adversarial Networks for modelling fuel sprays

In this study, the probabilistic, data driven nature of the generative adversarial neural networks (GANs) was utilized to conduct virtual spray simulations for conditions relevant to aero engine combustors. The model consists of two sub-modules: (i) an autoencoder converting the variable length droplet trajectories into fixed length, lower dimensional representations and (ii) a Wasserstein GAN that learns to mimic the latent representations of the evaporating droplets along their lifetime. The GAN module was also conditioned with the injection location and the diameters of the droplets to increase the generalizability of the whole framework. The training data was provided from highly resolved 3D, transient Eulerian–Lagrangian, large eddy simulations conducted with OpenFOAM. Neural network models were created and trained within the open source machine learning framework of PyTorch. Predictive capabilities of the proposed method was discussed with respect to spray statistics and evaporation dynamics. Results show that conditioned GAN models offer a great potential as low order model approximations with high computational efficiency. Nonetheless, the capabilities of the autoencoder module to preserve local dependencies should be improved to realize this potential. For the current case study, the custom model architecture was capable of conducting the simulation in the order of seconds after a day of training, which had taken one week on HPC with the conventional CFD approach for the same number of droplets (200,000 trajectories)

Generation of Synthetic Multi-Resolution Time Series Load Data

The availability of large datasets is crucial for the development of new power system applications and tools; unfortunately, very few are publicly and freely available. We designed an end-to-end generative framework for the creation of synthetic bus-level time-series load data for transmission networks. The model is trained on a real dataset of over 70 Terabytes of synchrophasor measurements spanning multiple years. Leveraging a combination of principal component analysis and conditional generative adversarial network models, the scheme we developed allows for the generation of data at varying sampling rates (up to a maximum of 30 samples per second) and ranging in length from seconds to years. The generative models are tested extensively to verify that they correctly capture the diverse characteristics of real loads. Finally, we develop an open-source tool called LoadGAN which gives researchers access to the fully trained generative models via a graphical interface