ASTIF: Adaptive Semantic-Temporal Integration for Cryptocurrency Price Forecasting

2025-12-21

AI Summary

To address the challenges of non-stationarity and multi-source heterogeneous information fusion in cryptocurrency forecasting, this paper proposes a semantic-temporal dual-channel adaptive prediction framework. It jointly models price time series and incorporates semantic signals—such as policy texts and market narratives—while introducing an uncertainty-aware, confidence-driven meta-learning inference layer that dynamically reweights the two channels and automatically shifts dominance during market turbulence, thereby enhancing robustness and interpretability. The framework integrates a MirrorPrompt-based dual-channel LLM, a hybrid LSTM–Random Forest temporal module, and a lightweight meta-learner. Evaluated on AI-themed cryptocurrency and technology stock data from 2020–2024, it significantly outperforms state-of-the-art models including Informer and TFT. Ablation studies demonstrate that the adaptive mechanism reduces prediction risk during volatile periods by 37%.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Metareasoning and Metaheuristics
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Security and Privacy: Cryptocurrency and smart contracts

Abstract

Financial time series forecasting is fundamentally an information fusion challenge, yet most existing models rely on static architectures that struggle to integrate heterogeneous knowledge sources or adjust to rapid regime shifts. Conventional approaches, relying exclusively on historical price sequences, often neglect the semantic drivers of volatility such as policy uncertainty and market narratives. To address these limitations, we propose the ASTIF (Adaptive Semantic-Temporal Integration for Cryptocurrency Price Forecasting), a hybrid intelligent system that adapts its forecasting strategy in real time through confidence-based meta-learning. The framework integrates three complementary components. A dual-channel Small Language Model using MirrorPrompt extracts semantic market cues alongside numerical trends. A hybrid LSTM Random Forest model captures sequential temporal dependencies. A confidence-aware meta-learner functions as an adaptive inference layer, modulating each predictor's contribution based on its real-time uncertainty. Experimental evaluation on a diverse dataset of AI-focused cryptocurrencies and major technology stocks from 2020 to 2024 shows that ASTIF outperforms leading deep learning and Transformer baselines (e.g., Informer, TFT). The ablation studies further confirm the critical role of the adaptive meta-learning mechanism, which successfully mitigates risk by shifting reliance between semantic and temporal channels during market turbulence. The research contributes a scalable, knowledge-based solution for fusing quantitative and qualitative data in non-stationary environments.

Problem

• Integrates semantic market cues with temporal data for forecasting
• Adapts forecasting strategy in real-time using confidence-based meta-learning
• Mitigates risk by adjusting reliance between data sources during turbulence

Innovation

• Adaptive meta-learning for real-time forecasting strategy
• Dual-channel language model extracts semantic market cues
• Hybrid LSTM Random Forest captures sequential dependencies