Table 18.

Challenges and future directions

Challenges	Future directions	Models	Examples
Extreme risk events disruption in cryptocurrency market	Study cryptocurrency market efficiency under high volatility or under shocks from other extreme events	Semi-supervised learning models and data streaming techniques	Ben Hamadou et al.227
Data shortage and over-reliance on CNN models in the cryptocurrency field	Improve language processing and image classification tasks	Data augmentation, transfer learning, recursive classification algorithms, few-shot learning and synthetic data generation	Li et al.228
Data quality and heterogeneity characteristics that may arise from the data	Aggregate multivariate heterogeneous data to achieve multi-level knowledge interaction	Agent-based, DAG-based learning	Lin et al.229
Acquisition and tracking of real-time transaction data	Take models to catch up with real-time data and react to it to quickly build investor confidence	High performance computing (HPC), as well as faster incremental learning, progressive neural networks	Murphy et al.230
Lack of accuracy in the prediction through deep learning algorithms	Choose algorithms based on their properties when applying them to various modeling tasks	Ensemble learning and other algorithms	Rama Rao et al.231
Ignorance of interactive behavior data across nodes or chains	Design smart contracts to model the dynamic characteristics of interaction behavior, between different data on the chain	Identification technique that can self-adapt to changes in the chain’s behavior	Du et al.232
Multi-objective, multi-level model design and application	Maximize agent utility by modeling multi-task learning, and focus on global optimum	Multi-objective architectures, transfer learning and encompass learning	Poyatos et al.233 and Ni et al.234