Prediction and decoding of metaverse coin dynamics: a granular quest using MODWT-Facebook’s prophet-TBATS and XAI methodology

IF 4.4 3区 管理学 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Indranil Ghosh, Amith Vikram Megaravalli, Mohammad Zoynul Abedin, Kazim Topuz
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引用次数: 0

Abstract

The growing media buzz and industry focus on the emergence and rapid development of Metaverse technology have paved the way for the escalation of multifaceted research. Specific Metaverse coins have come into existence, but they have barely seen any traction among practitioners despite their tremendous potential. The current work endeavors to deeply analyze the temporal characteristics of 6 Metaverse coins through the lens of predictive analytics and explain the forecasting process. The dearth of research imposes serious challenges in building the forecasting model. We resort to a granular prediction setup incorporating the Maximal Overlap Discrete Wavelet Transformation (MODWT) technique to disentangle the original series into subseries. Facebook's Prophet and TBATS algorithms are utilized to individually draw predictions on granular components. Aggregating components-wise forecasted figures achieve the final forecast. Facebook's Prophet is deployed in a multivariate setting, applying a set of explanatory features covering macroeconomic, technical, and social media indicators. Rigorous performance checks justify the efficiency of the integrated forecasting framework. Additionally, to interpret the black box typed prediction framework, two explainable artificial intelligence (XAI) frameworks, SHAP and LIME, are used to gauge the nature of the influence of the predictor variables, which serve several practical insights.

预测和解码虚拟货币动态:使用MODWT-Facebook的prophet-TBATS和XAI方法的颗粒任务
越来越多的媒体和行业关注元宇宙技术的出现和快速发展,为多方面研究的升级铺平了道路。特定的虚拟世界货币已经存在,但尽管它们具有巨大的潜力,但在从业者中几乎没有看到任何牵引力。目前的工作试图通过预测分析的视角深入分析6种元宇宙硬币的时间特征,并解释预测过程。研究的缺乏给建立预测模型带来了严峻的挑战。我们采用结合最大重叠离散小波变换(MODWT)技术的颗粒预测设置将原始序列分解为子序列。Facebook的Prophet和TBATS算法被用于在颗粒组件上单独绘制预测。聚合组件预测的数据实现最终的预测。Facebook的Prophet被部署在一个多变量环境中,应用了一套涵盖宏观经济、技术和社交媒体指标的解释性特征。严格的性能检查证明了集成预测框架的有效性。此外,为了解释黑箱类型的预测框架,使用了两个可解释的人工智能(XAI)框架,SHAP和LIME,来衡量预测变量影响的性质,这提供了一些实用的见解。
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来源期刊
Annals of Operations Research
Annals of Operations Research 管理科学-运筹学与管理科学
CiteScore
7.90
自引率
16.70%
发文量
596
审稿时长
8.4 months
期刊介绍: The Annals of Operations Research publishes peer-reviewed original articles dealing with key aspects of operations research, including theory, practice, and computation. The journal publishes full-length research articles, short notes, expositions and surveys, reports on computational studies, and case studies that present new and innovative practical applications. In addition to regular issues, the journal publishes periodic special volumes that focus on defined fields of operations research, ranging from the highly theoretical to the algorithmic and the applied. These volumes have one or more Guest Editors who are responsible for collecting the papers and overseeing the refereeing process.
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