Bahadur Yadav

Bahadur Yadav, Sanjay Kumar Mohanty

In this study, we present a quantum computing method that incorporates ridglet transforms into the quantum processing pipelines for time series data. Here, the Ridgelet neural network is integrated with a single-qubit quantum computing method, which improves feature extraction and forecasting capabilities. Furthermore, experimental results using financial time series data demonstrate the superior performance of our model compared to existing models.

Bahadur Yadav, Sanjay Kumar Mohanty

Forecasting stock prices remains challenging due to the volatile and non-linear nature of financial markets. Despite the promise of deep learning, issues such as mode collapse, unstable training, and difficulty in capturing temporal and feature level correlations have limited the applications of GANs in this domain. We propose a GRU-based Encoder-Decoder GAN (EDGAN) model that strikes a balance between expressive power and simplicity. The model introduces key innovations such as a temporal decoder with residual connections for precise reconstruction, conditioning on static and dynamic covariates for contextual learning, and a windowing mechanism to capture temporal dynamics. Here, the generator uses a dense encoder-decoder framework with residual GRU blocks. Extensive experiments on diverse stock datasets demonstrate that EDGAN achieves superior forecasting accuracy and training stability, even in volatile markets. It consistently outperforms traditional GAN variants in forecasting accuracy and convergence stability under market conditions.