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Paper+58+ (2025 7 2) +Stock+Market+Analysis+Using+Deep+Learning by ammara.cheema is a document available to read on EtoBox.

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This research article presents advanced transformer architectures, specifically TimeGPT and Spacetimeformer, for stock market prediction, achieving high accuracy rates of 96.2% for short-term and 94.8% for long-term forecasts. The models utilize self-attention mechanisms and temporal pattern recognition to process financial time series data efficiently, demonstrating significant improvements over traditional methods. The study highlights the effectiveness of these models in adapting to market volatility and

Author

ammara.cheema

Language

EN