Volatility and irregularity Capturing in stock price indices using time series Generative adversarial networks (TimeGAN)

This paper captures irregularities in financial time series data, particularly stock prices, in the presence of COVID-19 shock. We conjectured that jumps and irregularities are embedded in stock data due to the pandemic shock, which brings forth irregular trends in the time series data. We put forward that efficient and robust forecasting methods are needed to predict stock closing prices in the presence of the pandemic shock. This piece of information is helpful to investors as far as confidence risk and return boost are concerned. Generative adversarial networks of a time series nature are used to provide new ways of modeling and learning the proper and suitable distribution for the financial time series data under complex setups. Ideally, these traditional models are liable to producing high forecasting errors, and they need to be more robust to capture dependency structures and other stylized facts like volatility in stock markets. The TimeGAN model is used, effectively dealing with this risk of poor forecasts. Using the DAX stock index from January 2010 to November 2022, we trained the LSTM, GRU, WGAN, and TimeGAN models as benchmarks and forecasting errors were noted, and our TimeGAN outperformed them all as indicated by a small forecasting error.