A distributed stock exchange system encompasses multiple network hosts that participate in the sharing and exchange of resources. In such exchanges, the mediator or stock exchange must manage and delineate all operations in a cohesive manner. Stock exchange (SE) also acts as the transaction manager to provide consistent, isolated, durable, and atomic transactions for participating entities. However, the work for the stock exchange is not so straightforward as it may sound. With multiple transactions happening per second, the global serializability and concurrency control becomes an issue resulting in multiple threats and vulnerabilities. We propose a novel stock exchange that integrates time series prediction to distributed transactions and understanding the rapid global transactions and limitations of resources at the stock exchange. We use distributed acyclic graph (DAG) based distributed ledger technology IOTA to provide security and consensus for independent users. The paper proposes a time-variant model that adjusts its predictions based on transactions, moments of observations, participating entities, and history. We show that our model outcasts other state-of-art schemes in terms of prediction accuracy. Also, the model is fair, fast, and scalable to handle millions of transactions per second.

分布式证券交易所系统包含参与资源共享和交换的多个网络主机。在这种交换中，调解员或证券交易所必须以凝聚的方式管理和划定所有操作。股票交易所（SE）还充当事务管理器，为参与实体提供一致，隔离，持久和基本的事务。但是，股票交易所的工作听起来并不那么简单。由于每秒发生多个事务，因此全局可串行性和并发控制成为一个问题，导致多种威胁和漏洞。我们提出了一种新颖的证券交易所，该证券交易所将时间序列预测与分布式交易相集成，并了解快速的全球交易和证券交易所资源的局限性。我们使用基于分布式无环图（DAG）的分布式分类帐技术IOTA为独立用户提供安全性和共识。本文提出了一个时变模型，该模型根据交易，观察时刻，参与实体和历史记录来调整其预测。我们表明，在预测准确性方面，我们的模型优于其他最新方案。而且，该模型是公平，快速和可扩展的，每秒可处理数百万个事务。