Dynamic bagging-based ensemble method for machine-learning model selection in bitcoin price prediction

Name: Grittitee Grasaesom

keyword: Bitcoin forecasting

; Dynamic bagging

; Machine learning ensemble

; Time series prediction

; Decision support

Abstract: This study proposes a dynamic bagging ensemble that re-weights six baseline forecasters—ARIMA, SARIMA, ARIMAX, ANN, LSTM and BiLSTM—each day using rolling accuracy (MAE, MAPE, RMSE, R²), directional-accuracy and F1 scores derived from the most recent lookback window, while retaining static inverse-error weights learned during walk-forward cross-validation. When applied to out-of-sample BTC-USD data from six major exchanges (December 1, 2024 – April 1, 2025), the system achieves an average inference-stage MAE = 1545 USD, MAPE = 1.64%, RMSE = 2 060 USD, R² = 0.912, DA = 0.930 and F1 = 0.920, improving MAE by roughly 15% and DA by 3 - 5 percentage point relative to the best single-model benchmark. The ensemble also delivers a positive daily Sharpe ratio of ≈ 0.11 - 0.13 versus the 3-month U.S. Treasury-bill rate, whereas most individual learners turn negative under heavy-tuning scenarios. Selection-frequency analysis further reveals that statistical models dominate at short lookbacks (7–15 days) while LSTM-based networks gain prominence at 30-day horizons, underscoring the framework’s ability to adaptively allocate forecast weight to the most contextually effective model and thus provide a robust, low-latency decision-support tool for real-time Bitcoin trading.

Thammasat University. Thammasat University Library

Address: BANGKOK

Email: preserv@tu.ac.th

Name: Noptanit Chotisarn

Role: advisor

Created: 2025

Modified: 2025-11-26

Issued: 2025-11-26

วิทยานิพนธ์/Thesis

application/pdf

DOI: 10.14457/TU.the.2024.555

eng

DegreeName: Master of Science

Level: Master's Degree

Descipline: Management Information Systems

Grantor: Thammasat University

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