用可解释的模式识别，提升噪声金融市场的短期走势预测准确率。
From Patterns to Predictions: A Shapelet-Based Framework for Directional Forecasting in Noisy Financial Markets
- 先提取多变量时间序列中的不变模式，再用这些模式预测未来走势。
- 在比特币和标普500股票上12组测试中11组排名第一或第二。
- 能清晰展示决策依据，适合需要透明性的金融风控场景。
金融市场方向性预测需兼顾准确性与可解释性。深度学习虽能捕捉复杂动态，但透明度不足。本文提出两阶段框架：(i) SIMPC 将多变量时间序列分段聚类，提取对幅度缩放和时序扭曲鲁棒的重复模式，且不受窗口大小影响；(ii) JISC-Net 是基于形状基的分类器，以提取模式的前段为输入，预测后续部分序列的短期方向。在比特币及三只标普500成分股上的实验表明，本方法在12组（指标-数据集）组合中，有11组位列第一或第二，显著优于基线模型。与传统深度学习仅输出买卖信号不同，该方法通过揭示驱动预测结果的底层模式结构，实现透明决策。
原文摘要 · Abstract (English)
Directional forecasting in financial markets requires both accuracy and interpretability. Before the advent of deep learning, interpretable approaches based on human-defined patterns were prevalent, but their structural vagueness and scale ambiguity hindered generalization. In contrast, deep learning models can effectively capture complex dynamics, yet often offer limited transparency. To bridge this gap, we propose a two-stage framework that integrates unsupervised pattern extracion with interpretable forecasting. (i) SIMPC segments and clusters multivariate time series, extracting recurrent patterns that are invariant to amplitude scaling and temporal distortion, even under varying window sizes. (ii) JISC-Net is a shapelet-based classifier that uses the initial part of extracted patterns as input and forecasts subsequent partial sequences for short-term directional movement. Experiments on Bitcoin and three S&P 500 equities demonstrate that our method ranks first or second in 11 out of 12 metric--dataset combinations, consistently outperforming baselines. Unlike conventional deep learning models that output buy-or-sell signals without interpretable justification, our approach enables transparent decision-making by revealing the underlying pattern structures that drive predictive outcomes.
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