用语言模型+进化算法自动做时间序列特征工程，提升预测准确率8.4%。
ELATE: Evolutionary Language model for Automated Time-series Engineering
- 结合语言模型与进化框架生成新特征
- 在多个领域平均提升预测准确率8.4%
- 适合需要高效特征工程的时间序列研究者
时间序列预测依赖机器学习模型对未来值进行预估。特征工程通过转换现有特征生成新特征，对提升模型性能至关重要，但通常为人工操作且耗时。现有自动化方法依赖穷举枚举，计算成本高且缺乏领域知识。我们提出ELATE（基于进化语言模型的时间序列自动化特征工程），将语言模型嵌入进化框架，自动完成时间序列特征工程。ELATE利用时间序列统计量和特征重要性指标指导并修剪特征，同时由语言模型提出上下文相关的特征变换。实验表明，ELATE在多个领域平均提升预测准确率8.4%。
原文摘要 · Abstract (English)
Time-series prediction involves forecasting future values using machine learning models. Feature engineering, whereby existing features are transformed to make new ones, is critical for enhancing model performance, but is often manual and time-intensive. Existing automation attempts rely on exhaustive enumeration, which can be computationally costly and lacks domain-specific insights. We introduce ELATE (Evolutionary Language model for Automated Time-series Engineering), which leverages a language model within an evolutionary framework to automate feature engineering for time-series data. ELATE employs time-series statistical measures and feature importance metrics to guide and prune features, while the language model proposes new, contextually relevant feature transformations. Our experiments demonstrate that ELATE improves forecasting accuracy by an average of 8.4% across various domains.
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