Intelligent Forecast of Stock Markets to Handle COVID-19 Economic Crisis by Modified Generative Adversarial Networks

Stock markets have voluminous data and are subjected to uncertainty. The coronavirus disease of 2019 (COVID-19) pandemic has hit the stock markets and the trends of stock markets have accelerated share prices of few companies and has also brought freefall to certain companies. This factor highlights the importance of technical analysis of the stock markets over fundamental analysis. So, the proposed robust model for financial forecasting is built based on the technical indicators and the fake price data generated over a period of time from the stock dataset by a novel architecture of modified generative adversarial network, which uses a dense recurrent neural network as the generator and a dense spectrally normalized convolutional neural network as the discriminator. The hyperparameters used in the network model follow the two-time-scale-update rule and they are tuned by using the Bayesian optimization technique. The feature importance of the technical indicators in predicting the performance by the stock market is enhanced by the XGBoost algorithm. The generative adversarial networks (GAN) used for forecasting in the previous works suffer from problems like mode collapse and non-convergence. So, the proposed work concentrates on building a GAN model, which is stable, robust and converges to Nash equilibrium. The generated GAN model is applied on stock data from the major 100 companies of the S&P 500 stock for a period of 20 years. The modified GAN model predicts prices precise similar to 99 percentage, which maximizes the stock returns. The proposed modified GAN model outperforms the baseline GAN model and other state of the art approaches of forecasting on comparison.

Network Delay Modelling and Optimization of Internet-Based Distributed Test Platform for Fuel Cell Electric Vehicle Powertrain System

The accelerated global progress in the research and development of automobile products, and the use of new technologies, such as the Internet, cloud computing and big data, to coordinate development platforms in different regions and fields, can reduce the duration and cost of development and testing. Specifically, in the context of the current coronavirus disease (COVID-19) pandemic, which has caused great obstacles to normal logistics and transportation, personnel exchanges and information communication, platforms that can support global operation are significant for product testing and validation, because they eliminate the need for the transportation of personnel and equipment. Therefore, the establishment of a distributed test and validation platform for automotive powertrain systems, which can integrate software and hardware testing, is important in terms of both scientific research and industrialization. The main technical difficulties associated with such test and validation platforms include data transmission and the control of the transmission effect. A distributed test and validation platform for a fuel cell electric vehicle powertrain system is proposed herein. The two-time-scale Markov chain is used to simulate the delay between two places (China and Germany), and the least-squares support vector machine (LSSVM) method is used to optimize the transmission effect. The results show that the two-time-scale Markov chain model can effectively simulate the delay between two nations, and that its probability distribution is close to the measured value. The LSSVM method is effectively optimized for all four indicators (velocity, fuel cell output power, battery output power and electric motor output torque). This method can be effectively used in the remote development test validation of vehicle powertrain system. © 2021 SAE Technical Papers. All rights reserved.