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深度学习方法 LSTM 与随机游走模型在金融和医疗领域应用的比较：以 COVID-19 发展预测为例。

A Comparison on LSTM Deep Learning Method and Random Walk Model Used on Financial and Medical Applications: An Example in COVID-19 Development Prediction.

机构信息

School of Fintech, Hebei Finance University, Baoding, China.

Finance Department, Capital University of Economics and Business, Beijing, China.

出版信息

Comput Intell Neurosci. 2022 Aug 23;2022:4383245. doi: 10.1155/2022/4383245. eCollection 2022.

DOI:10.1155/2022/4383245
PMID:36052038
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9427226/
Abstract

This study aims to establish the model of the cryptocurrency price trend based on a financial theory using the Long Short-Term Memory (LSTM) networks model with multiple combinations between the window length and the predicting horizons. The Random Walk model is also applied with different parameter settings. The object of this study is the cryptocurrency and medical issues, primarily the Bitcoin and Ethereum and the COVID-19. Quantitative analysis is adopted as the method of this dissertation. The research tool is Python programming language, and the TensorFlow package is employed to model and analyze research topics. The results of this study show the limitations of the LSTM and Random Walk model for price prediction while demonstrating the different characteristics of both models with different parameter settings, providing a balance between the model's accuracy and the model's practicality.

摘要

本研究旨在基于金融理论，使用具有多种窗口长度和预测范围组合的长短时记忆（LSTM）网络模型，建立加密货币价格趋势模型。同时，还应用了随机漫步模型，并设置了不同的参数。本研究的对象是加密货币和医疗问题，主要是比特币和以太坊以及 COVID-19。本论文采用定量分析作为方法。研究工具是 Python 编程语言，并使用 TensorFlow 包对研究主题进行建模和分析。本研究的结果表明 LSTM 和随机漫步模型在价格预测方面的局限性，同时展示了不同参数设置下两种模型的不同特征，在模型准确性和实用性之间取得了平衡。

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本文引用的文献

1
Evolutionary dynamics of the cryptocurrency market.加密货币市场的演化动态。
R Soc Open Sci. 2017 Nov 15;4(11):170623. doi: 10.1098/rsos.170623. eCollection 2017 Nov.
2
Design and analysis of self-adapted task scheduling strategies in wireless sensor networks.无线传感器网络中自适应任务调度策略的设计与分析。
Sensors (Basel). 2011;11(7):6533-54. doi: 10.3390/s110706533. Epub 2011 Jun 27.