Real-world model for bitcoin price prediction

【Author】 Rathore, Rajat Kumar; Mishra, Deepti; Mehra, Pawan Singh; Pal, Om; Hashim, Ahmad sobri; Shap'i, Azrulhizam; Ciano, T.; Shutaywi, Meshal

【Source】INFORMATION PROCESSING & MANAGEMENT

【Abstract】Cryptocurrency is a new sort of digital asset that has evolved as a result of advances in financial technology, and it has provided a significant research opportunity. There are many algorithms for price prediction for crypto currencies like LSTM and ARIMA. However, the downside is that LSTM-based RNNs are difficult to comprehend, and gaining intuition into their behavior is tough. In order to produce decent outcomes, rigorous hyperparameter adjustment is also essential. Furthermore, crypto currencies do not precisely adhere to past data, and patterns change fast, reducing the accuracy of predictions. Cryptocurrency price forecasting is difficult due to price volatility and dynamism. Because the data is dynamic and heavily influenced by various seasons, the ARIMA model is unable to handle seasonal data. In order to provide better price predictions for crypto traders, a new model is required. The objective of the study is to apply Fbprophet model as the key model because it is superior in functionality as compared to LSTM and ARIMA additionally removing the pitfalls generated in LSTM and ARIMA model while analyzing the cryptocurrency data. This study provides a methodology for predicting the future price of bitcoin that does not rely solely on past data due to seasonality in historical data. So, after fitting the seasonality and smoothing, the model is constructed that can be useful for real-world use cases. In case of crypto currencies where less historical data is available and it is hard to find pattern, proposed method can easily deal this type of problems. Overall difference between predicted and actual values is low as compared to other model even after seasonal data was available.

【Keywords】Bitcoin; Cryptocurrency; Machine learning; Prediction; Time series analysis; Fbprophet model

【标题】比特币价格预测的真实世界模型

【摘要】加密货币随着金融技术的进步而发展起来，是一种新型的数字资产，它提供了重要的研究机会。 LSTM 和 ARIMA 等加密货币的价格预测算法有很多。然而，这些算法的缺点是难以理解基于 LSTM 的RNN算法，并且很难直观地了解它们的行为。为了产生准确的结果，严格的超参数调整也是必不可少的。此外，加密货币不能精确地遵循过去的数据，模式变化很快，降低了预测的准确性。由于价格波动和动态因素，加密货币的价格预测很困难。由于数据是动态的，并且受各个季节的影响很大，因此ARIMA模型无法处理季节性数据。为了更好地预测加密货币的价格，需要一个新模型。本研究的目的是应用Fbprophet模型作为主要预测模型，因为与LSTM和ARIMA相比，它在功能上更胜一筹，此外还消除了LSTM和ARIMA模型在分析加密货币数据时产生的缺陷。本研究提供了一种预测比特币未来价格的方法，由于历史数据的季节性因素，它不仅仅依赖于过去的数据。因此，在拟合季节性、平滑调节之后，构建的模型可用于实际情况。在加密货币可用历史数据较少且难以找到模型匹配的情况下，我们所提出的方法可以轻松处理此类问题。即使在季节性数据可用之后，与其他模型相比，本模型的预测值和实际值之间的总体差异也很小。

【关键词】比特币；加密货币；机器学习；预测；时间序列分析；Fbprophet模型

【发表时间】2022

【收录时间】2022-07-15

【文献类型】Article

【论文大主题】加密货币

【论文小主题】市场分析与预测

【影响因子】7.466

【翻译者】张宵霆