Aug 2026· Evolutionary Systematics· Vol 17· 0 citations· 77 references
TL;DR
This study introduces a robust network-oriented framework for forecasting cryptocurrency price dynamics, leveraging visibility graph-based reconstructions to capture temporal structures and dependencies in market data and uses Local Interpretable Model-agnostic Explanations (LIME) to reveal how key network metrics drive price predictions, providing transparency and reliability in high-risk markets.
Cryptocurrency forecasting presents a distinctive combination of extreme cross-asset scale heterogeneity, non-stationary dynamics, and structural dependencies among Open, High, Low, and Close (OHLC) variables. We present CryptoL, a unified framework designed to address these challenges within multivariate time-series f...
Yalda Taheri, Mohammad Hassan Heydari, Armon Rasooli et al.· 0 citations
The high volatility and complexity of cryptocurrency markets create difficulties for investors and researchers who
attempt to make accurate price predictions. This project presents an intelligent and data-driven approach for
cryptocurrency analysis using Long ShortTerm Memory (LSTM) networks, a specialized type of Recu...
D. Rahul, M. Shiva, Parag Ravikant Kaveri et al.· International Journal of Inn...· 0 citations
Accurate cryptocurrency price forecasting represents an important yet challenging problem in financial time-series analysis due to the highly volatile, nonlinear, and noise-sensitive nature of digital asset markets. Although transformer-based architectures have recently demonstrated strong capabilities in temporal sequ...
This study presents a novel multi-source, multi-output deep learning framework designed to predict the direction of price changes in four major cryptocurrencies immediately after overreaction events, highlighting the benefits of joint modeling, leading to more reliable forecasts during turbulent market conditions.
Marjan Sadat Fatemi Ghomi, A. Saghaei, Majid Mirzaee Ghazani· International Journal of Fin...· 0 citations
Detecting spoofing in financial trading is a critical data mining task. While traditional machine learning models focus on individual node features, graph-based methodologies have shown superior performance by integrating relational data and structure information. However, spoofing transactions often exhibit distributi...
Sheng Xiang, Zi-Wen Xu, Yidong Jiang et al.· Proceedings of the Thirty-Fi...· 0 citations
Graph topology and model architecture are routinely co-designed in GNN-based fraud detection, making it impossible to attribute performance gains to either component. We address this by fixing the training loop, features, and evaluation protocol while independently varying the graph construction strategy and GNN archit...
Roya Amiri, Sardar F. Jaf· Big Data and Cognitive Compu...· 0 citations
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