📄 中文摘要
机器学习在预测领域被广泛应用，但并非所有数据的行为相同。常见的错误是将标准机器学习应用于时间依赖数据，而不考虑时间顺序和依赖性，这些模型并不能自然捕捉这些特性。时间序列数据反映了随时间演变的模式，而静态快照则不同。例如，销售预测与违约风险的分析存在显著差异。理解时间序列与标准机器学习之间的区别，有助于选择合适的方法进行数据分析和预测。
时间序列与标准机器学习：关键区别、应用场景及示例
📄 English Summary
Time Series vs Standard Machine Learning: Key Differences, Use Cases, and Examples
Machine learning is extensively used for prediction, but not all data behaves similarly. A common mistake is applying standard machine learning to time-dependent data without considering temporal order and dependencies, which these models do not inherently capture. Time series data reflects evolving patterns over time, in contrast to static snapshots. For instance, sales forecasting differs significantly from default risk analysis. Understanding the distinctions between time series and standard machine learning is crucial for selecting the appropriate methods for data analysis and prediction.