Non Fungible Tokens (NFT) and Decentralized Finance (DeFi) assets have seen a growing media coverage and garnered considerable investor traction despite being classified as a niche in the digital financial sector. The lack of substantial research to demystify the dynamics of NFT and DeFi coins motivates the scrupulous analysis of the said sector. This work aims to critically delve into the evolutionary pattern of the NFTs and DeFis for performing predictive analytics of the same during the COVID-19 regime. The multivariate framework comprises the systematic inclusion of explanatory features embodying technical indicators, key macroeconomic indicators, and constructs linked to media hype and sentiment pertinent to the pandemic, nonlinear feature engineering, and ensemble machine learning. Isometric Mapping (ISOMAP) and Uniform Manifold Approximation and Projection (UMAP) techniques are conjugated with Gradient Boosting Regression (GBR) and Random Forest (RF) for enabling the predictive analysis. The predictive performance rationalizes the frameworks' capacity to accurately predict the prices of the majority of the NFT and DeFi coins during the ongoing financial distress period. Additionally, Explainable Artificial Intelligence (XAI) methodologies are used to comprehend the nature of the impact of the explanatory variables. Findings suggest that the daily movement of the NFTs and DeFi highly depends on their past historical movement.

尽管被归类为数字金融领域的利基市场，但不可替代代币 (NFT) 和去中心化金融 (DeFi) 资产的媒体报道越来越多，并获得了相当大的投资者吸引力。缺乏大量研究来揭开 NFT 和 DeFi 代币动态的神秘面纱，促使对该行业进行谨慎的分析。这项工作旨在批判性地研究 NFT 和 DeFis 的进化模式，以便在 COVID-19 期间对其进行预测分析。多变量框架包括系统地包含解释性特征，包括技术指标、关键宏观经济指标，以及与媒体炒作和与大流行相关的情绪、非线性特征工程和集成机器学习相关的结构。等距映射 (ISOMAP) 和均匀流形逼近和投影 (UMAP) 技术与梯度提升回归 (GBR) 和随机森林 (RF) 相结合，以实现预测分析。预测性能合理化了框架在持续的财务困境期间准确预测大多数 NFT 和 DeFi 代币价格的能力。此外，可解释人工智能 (XAI) 方法用于理解解释变量影响的性质。调查结果表明，NFT 和 DeFi 的日常走势在很大程度上取决于它们过去的历史走势。预测性能合理化了框架在持续的财务困境期间准确预测大多数 NFT 和 DeFi 代币价格的能力。此外，可解释人工智能 (XAI) 方法用于理解解释变量影响的性质。调查结果表明，NFT 和 DeFi 的日常走势在很大程度上取决于它们过去的历史走势。预测性能合理化了框架在持续的财务困境期间准确预测大多数 NFT 和 DeFi 代币价格的能力。此外，可解释人工智能 (XAI) 方法用于理解解释变量影响的性质。调查结果表明，NFT 和 DeFi 的日常走势在很大程度上取决于它们过去的历史走势。