Forecasting Bitcoin prices using artificial intelligence:
Combination of ML, SARIMA, and Facebook Prophet models
✍️ Lupin moha
link.springer.com
Deep learning for Bitcoin price direction prediction: models and trading strategies empirically compared
Oluwadamilare Omole, David Enke
Financial Innovation 10 (1), 117, 2024
This paper applies deep learning models to predict Bitcoin price directions and the subsequent profitability of trading strategies based on these predictions. The study compares the performance of the convolutional neural network–long short-term memory (CNN–LSTM), long- and short-term time-series network, temporal convolutional network, and ARIMA (benchmark) models for predicting Bitcoin prices using on-chain data. Feature-selection methods—i.e., Boruta, genetic algorithm, and light gradient boosting machine—are applied to address the curse of dimensionality that could result from a large feature set. Results indicate that combining Boruta feature selection with the CNN–LSTM model consistently outperforms other combinations, achieving an accuracy of 82.44%. Three trading strategies and three investment positions are examined through backtesting. The long-and-short buy-and-sell investment approach generated an extraordinary annual return of 6654% when informed by higher-accuracy price-direction predictions. This study provides evidence of the potential profitability of predictive models in Bitcoin trading.
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Forecasting Bitcoin prices using artificial intelligence: Combination of ML, SARIMA, and Facebook Prophet models
Lupin moha
Technological Forecasting and Social Change 198, 122938, 2024
In recent years, investors, corporations, and enterprises have shown great interest in the Bitcoin network; thus, promoting its products and services is crucial. This study utilizes an empirical analysis for financial time series and machine learning to perform prediction of bitcoin price and Garman-Klass (GK) volatility using Long Short-Term Memory (LSTM), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Facebook prophet models. The performance findings show that the LTSM boost has a noticeable improvement compared to SARIMA and Facebook Prophet in terms of MSE (Mean Squared Error) and MAE (Mean Average Error). Unlike Long Short-Term Memory (LSTM), a component of Deep Learning (DL), the finding explains why the bitcoin and its volatility forecasting difficulty has been partially met by traditional time series forecasting (SARIMA) and auto-machine-learning technique (Fb-Prophet). Furthermore, the finding confirmed that Bitcoin values are extremely seasonally volatile and random and are frequently influenced by external variables (or news) such as cryptocurrency laws, investments, or social media rumors. Additionally, results show a robust optimistic trend, and the days when most people commute are Monday and Saturday and an annual seasonality. The trend of the price and volatility of bitcoin using SARIMA and FB-Prophet is more predictable. The Fb-Prophet cannot easily fit within the Russian-Ukrainian conflict period, and in some COVID-19 periods, its performance will suffer during the turbulent era. Moreover, Garman-Klass (GK) forecasting seems more effective than the squared returns price measure, which has implications for investors and fund managers. The research presents innovative insights pertaining to forthcoming cryptocurrency regulations, stock market dynamics, and global resource allocation
