OracleMamba: A Dynamic Market-Guided and Time State Selection Framework for Robust Stock Prediction
Keywords: deep learning, time series
Abstract: Stock price prediction is a complex challenge due to the inherent volatility of financial markets and the influence of diverse factors such as macroeconomic conditions, capital flows, and market sentiment. Recent joint stock forecasting models focus on extracting temporal patterns from individual stock price series and combining them to model stock correlations. However, these models face two critical limitations: first, in long-term predictions, they retain both informative and excessive states, amplifying noise and increasing complexity; second, in short-term predictions, they prioritize market indices and technical indicators, neglecting the real-time influence of market sentiment, which can drive price movements independent of traditional indicators. While state space models (SSMs) like Mamba improve efficiency and capture long-distance relationships, they still underperform compared to Transformer-based models.
To address these challenges, we propose OracleMamba, a novel framework that integrates a dynamic market-guided module for short-term forecasting and a SelectiveMamba module for long-term forecasting. The dynamic market-guided module fuses objective market data and subjective sentiment analysis to enhance short-term prediction accuracy. The SelectiveMamba module efficiently captures both spectral and temporal features using a 3D scan mechanism, which extracts and filters key signals from the time-series data. By integrating spectral features to identify market rhythms and temporal features to track price movements over time, the SelectiveMamba module reduces noise and preserves critical information for long-term forecasts. This framework significantly improves both model efficiency and accuracy, outperforming existing approaches across real-world stock prediction tasks.
Primary Area: learning on time series and dynamical systems
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Submission Number: 868
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