Verifying Technical Indicator Effectiveness in Cryptocurrency Price Forecasting: a Deep-Learning Time Series Model Based on Sparrow Search Algorithm

【Author】 Cheng, Ching-Hsue; Yang, Jun-He; Dai, Jia-Pei

【Source】COGNITIVE COMPUTATION

【影响因子】4.890

【Abstract】Forecasting cryptocurrency prices is challenging due to market volatility and dynamic behavior. This study aims to enhance prediction accuracy for Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC) by proposing a novel deep learning framework. The framework integrates the Sparrow Search Algorithm (SSA) for selecting optimal technical indicators with Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Technical indicators derived from historical market data, including prices and trading volume, are analyzed to improve forecasting. The results demonstrate that the proposed framework effectively enhances prediction accuracy for BTC and LTC. For ETH, the best performance is achieved using all 34 indicators with the Bi-LSTM model. These findings highlight the importance of selecting relevant indicators and demonstrate the potential of advanced deep learning models in addressing the complexities of cryptocurrency markets. This research provides valuable insights and a reliable framework for improving cryptocurrency price predictions.

【Keywords】Cryptocurrency Price prediction; Technical Indicator selection; Sparrow search algorithm (SSA); Bidirectional long short-term memory (bi-LSTM); Time series forecasting; Deep learning

【发表时间】2025 FEB

【收录时间】2025-04-08

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