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arXiv Machine Learning By Wilfredo Tovar

Deep Learning Based on Generative Adversarial and Convolutional Neural Networks for Financial Time Series Predictions

Read the original on arXiv Machine Learning →

The paper proposes a hybrid generative adversarial network (GAN) that combines a bi-directional LSTM and a CNN (Bi‑LSTM‑CNN) to generate synthetic financial data aligned with real market data. By preserving stock trend features, the model predicts future stock price movements across multiple markets (TSX, SHCOMP, S&P 500). Experiments show that this hybrid approach outperforms existing machine‑learning prototypes, and the study highlights gaps between investors and technical researchers.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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