用符号与拓扑方法分析搜索趋势，更好识别消费者行为模式。
The Shape of Consumer Behavior: A Symbolic and Topological Analysis of Time Series
- 将搜索时间序列转为符号串或拓扑结构进行聚类
- 拓扑方法比传统符号法更稳定，能区分复杂波动模式
- 适合做实时营销和趋势预测的分析师参考
理解在线搜索行为的时间模式对实时营销和趋势预测至关重要。谷歌趋势提供了公众兴趣的丰富代理数据，但其高维性和噪声给有效聚类带来挑战。本研究评估了三种无监督聚类方法：符号聚合近似（SAX）、增强型SAX（eSAX）和拓扑数据分析（TDA），应用于20个代表主要消费类别的谷歌趋势关键词。结果表明，虽然SAX和eSAX在稳定时间序列上提供快速且可解释的聚类，但在波动性和复杂性场景下表现不佳，常产生模糊的“万能”聚类。相比之下，TDA通过持久同调捕捉全局结构特征，实现了更均衡且有意义的分组。研究最后给出符号与拓扑方法在消费者分析中的实用建议，并指出结合两者视角的混合方法在未来应用中具有强大潜力。
原文摘要 · Abstract (English)
Understanding temporal patterns in online search behavior is crucial for real-time marketing and trend forecasting. Google Trends offers a rich proxy for public interest, yet the high dimensionality and noise of its time-series data present challenges for effective clustering. This study evaluates three unsupervised clustering approaches, Symbolic Aggregate approXimation (SAX), enhanced SAX (eSAX), and Topological Data Analysis (TDA), applied to 20 Google Trends keywords representing major consumer categories. Our results show that while SAX and eSAX offer fast and interpretable clustering for stable time series, they struggle with volatility and complexity, often producing ambiguous ``catch-all'' clusters. TDA, by contrast, captures global structural features through persistent homology and achieves more balanced and meaningful groupings. We conclude with practical guidance for using symbolic and topological methods in consumer analytics and suggest that hybrid approaches combining both perspectives hold strong potential for future applications.
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