Stock Market Analysis Using Deep Learning

Abstract

This research explores advanced transformer architectures for stock market prediction, focusing on TimeGPT and Spacetimeformer models. We implement sophisticated time-series transformers that leverage self-attention mechanisms and temporal pattern recognition to enhance prediction accuracy. Our methodology combines multi-layered transformer pipelines with specialized market-specific encodings and quantum-inspired computing elements. Testing across diverse market conditions demonstrates significant improvements over traditional approaches, achieving accuracy rates of 96.2% in short-term predictions and 94.8% in long-term forecasting. The system processes financial time series data through multi-head attention layers while maintaining sub-millisecond prediction times, establishing new benchmarks in market prediction performance. This work contributes novel techniques for handling market volatility and regime changes, with particular strength in adapting to extreme market events.