Temporal Attention-Enhanced Stacking Networks: Revolutionizing Multi-Step Bitcoin Forecasting

Phumudzo Lloyd Seabe (), Edson Pindza, Claude Rodrigue Bambe Moutsinga and Maggie Aphane
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Phumudzo Lloyd Seabe: Department of Mathematics and Applied Mathematics, Sefako Makgatho Health Sciences University, Pretoria 0204, South Africa
Edson Pindza: College of Economic and Management Sciences, Department of Decision Sciences, University of South Africa, Pretoria 0002, South Africa
Claude Rodrigue Bambe Moutsinga: Department of Mathematics and Applied Mathematics, Sefako Makgatho Health Sciences University, Pretoria 0204, South Africa
Maggie Aphane: Department of Mathematics and Applied Mathematics, Sefako Makgatho Health Sciences University, Pretoria 0204, South Africa

Forecasting, 2024, vol. 7, issue 1, 1-28

Abstract: This study presents a novel methodology for multi-step Bitcoin (BTC) price prediction by combining advanced stacking-based architectures with temporal attention mechanisms. The proposed Temporal Attention-Enhanced Stacking Network (TAESN) integrates the complementary strengths of diverse machine learning algorithms while emphasizing critical temporal features, leading to substantial improvements in forecasting accuracy over traditional methods. Comprehensive experimentation and robust evaluation validate the superior performance of TAESN across various BTC prediction horizons. Additionally, the model not only demonstrates enhanced predictive accuracy but also offers interpretable insights into the temporal dynamics underlying cryptocurrency markets, contributing to both practical forecasting applications and theoretical understanding of market behavior.

Keywords: cryptocurrency price forecasting; temporal attention mechanism; LSTM; GRU; multi-step prediction; stacking ensemble learning; Temporal Convolutional Networks (TCNs); hybrid machine learning models (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2024
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