用决策树自动预测时间序列，效果媲美传统统计模型。
Automated univariate time series forecasting with regression trees
- 基于自回归和递归预测，用决策树捕捉时序规律
- 在真实数据上准确率接近指数平滑与ARIMA模型
- 开源工具支持全流程自动化，适合工业场景应用
本文提出一种基于回归树及其集成方法（袋装法与随机森林）的自动化单变量时间序列预测方法。重点解决自回归特征选择、趋势序列处理及季节性行为建模等问题。实验结果表明，该方法预测精度可与经典的指数平滑和ARIMA等统计模型相媲美。此外，论文还开发并公开了一个实现所有策略的软件工具。
原文摘要 · Abstract (English)
This paper describes a methodology for automated univariate time series forecasting using regression trees and their ensembles: bagging and random forests. The key aspects that are addressed are: the use of an autoregressive approach and recursive forecasts, how to select the autoregressive features, how to deal with trending series and how to cope with seasonal behavior. Experimental results show a forecast accuracy comparable with well-established statistical models such as exponential smoothing or ARIMA. Furthermore, a publicly available software implementing all the proposed strategies has been developed and is described in the paper.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。