Predicting Top Five Cryptocurrency Prices via Linear Structural Time Series (STS) Approach

IF 0.3 Q4 MATHEMATICS
Nur Maisarah Abdul Rashid, M. Ismail, Noor Wahida Md Junus
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引用次数: 0

Abstract

Predicting cryptocurrency prices are difficult due to dynamic data. At the same time, the hidden market behavior of trend and seasonal components in the history data is also critical as it provides an idea of what the price pattern will be in the future. Hence, this research proposes to identify and model the hidden pattern behavior in terms of component time series instead of removing it via the linear structural time series (STS) model approach. This study focuses on the top five cryptocurrencies relying on the highest market capitalization. From the results obtained, the top five cryptocurrencies have a different trend model, either deterministic or stochastic, which relies on the behavior of data. The five cryptocurrencies also show the crypto winter event, where the trend is downward after six months every year. The linear STS is the best model for predicting three cryptocurrencies’ prices for nonstationary and volatility data behavior. It can also handle the hidden component behavior and is easy to interpret. Since the linear STS model can indirectly retain the information of data, it will assist investors and traders in accurately predicting cryptocurrency prices.
通过线性结构时间序列(STS)方法预测五大加密货币价格
由于数据动态,预测加密货币价格很困难。与此同时,历史数据中趋势和季节性成分的隐藏市场行为也至关重要,因为它提供了未来价格模式的概念。因此,本研究提出根据组件时间序列来识别和建模隐藏模式行为,而不是通过线性结构时间序列(STS)模型方法来去除它。这项研究的重点是依赖最高市值的前五大加密货币。从获得的结果来看,排名前五的加密货币有一个不同的趋势模型,要么是确定性的,要么是随机的,这取决于数据的行为。这五种加密货币还显示了加密货币冬季事件,每年六个月后,这一趋势呈下降趋势。对于非平稳和波动性数据行为,线性STS是预测三种加密货币价格的最佳模型。它还可以处理隐藏的组件行为,并且易于解释。由于线性STS模型可以间接保留数据的信息,它将帮助投资者和交易员准确预测加密货币价格。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Matematika
Matematika MATHEMATICS-
自引率
25.00%
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审稿时长
24 weeks
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