This video presents our recent research on financial volatility forecasting, comparing classical econometric models, signal-processing techniques, and modern machine-learning approaches.
The study evaluates the forecasting performance, volatility prediction accuracy, computational efficiency, and interpretability of five different methods:
• GARCH
• SARIMA
• Matrix Pencil (MP)
• XGBoost
• Long Short-Term Memory (LSTM)
Using Bitcoin returns as a high-volatility financial asset, we investigate whether modern AI-based methods consistently outperform traditional forecasting techniques when accuracy, explainability, and computational cost are considered simultaneously.
Our results show that:
✓ SARIMA achieves the lowest return forecasting error.
✓ Matrix Pencil delivers comparable forecasting accuracy with extremely low computational cost.
✓ XGBoost provides a strong balance between accuracy and efficiency.
✓ LSTM exhibits relatively strong performance in tracking volatility patterns and market regime changes, despite higher forecasting errors and computational requirements.
✓ No single model dominates across all evaluation criteria.
The findings suggest that the Matrix Pencil method remains a competitive and highly interpretable alternative to black-box machine-learning models for financial forecasting applications.
NicoNico: https://www.nicovideo.jp/watch/sm46580360
Sakuya, Youmu, and Marisa challenge a unique cooking competition!
Using Bitcoin market data as ingredients, five dishes — GARCH, SARIMA, Matrix Pencil, XGBoost, and LSTM — to compete to predict future market movements ♬
This video explains the strengths and characteristics of traditional econometric models, signal-processing approaches, and modern AI techniques through a fun Yukkuri-style commentary.
Which model will create the best “forecasting recipe” for the future market?
Artwork including original character illustrations:
https://www.pixiv.net/en/tags/%E5%A6%...
Music, sound effects, and background materials used in this video are credited in the end credits.
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