Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices
面向数据科学家的预测评估：常见陷阱与最佳实践AI 解读 ↓
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面向数据科学家的预测评估：常见陷阱与最佳实践
- 研究问题
- 面对非平稳、非正态等时间序列特征，机器学习预测应如何进行有效评估，避免数据划分、误差度量和检验中的常见陷阱？
- 方法与思路
- 作者以教程式综述整理预测评估流程，连接传统统计与计量知识，并按数据划分、误差计算、统计检验和指标选择提出建议。
- 材料陈述的贡献
- 摘要所述贡献是系统汇编预测评估注意事项，解释时间序列特征与评估陷阱的关系，并指导选择适合数据特点的误差指标。
- 阅读关注点
- 应核对各项建议适用的预测任务、预测期限与数据结构，尤其关注时间顺序划分、指标选择依据和统计比较的有效条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列出具体建议或验证结果，不能把教程概括视为新方法的性能证明。
原文摘要
Machine Learning (ML) and Deep Learning (DL) methods are increasingly replacing traditional methods in many domains involved with important decision making activities. DL techniques tailor-made for specific tasks such as image recognition, signal processing, or speech analysis are being introduced at a fast pace with many improvements. However, for the domain of forecasting, the current state in the ML community is perhaps where other domains such as Natural Language Processing and Computer Vision were at several years ago. The field of forecasting has mainly been fostered by statisticians/econometricians; consequently the related concepts are not the mainstream knowledge among general ML practitioners. The different non-stationarities associated with time series challenge the data-driven ML models. Nevertheless, recent trends in the domain have shown that with the availability of massive amounts of time series, ML techniques are quite competent in forecasting, when related pitfalls are properly handled. Therefore, in this work we provide a tutorial-like compilation of the details of one of the most important steps in the overall forecasting process, namely the evaluation. This way, we intend to impart the information of forecast evaluation to fit the context of ML, as means of bridging the knowledge gap between traditional methods of forecasting and state-of-the-art ML techniques. We elaborate on the different problematic characteristics of time series such as non-normalities and non-stationarities and how they are associated with common pitfalls in forecast evaluation. Best practices in forecast evaluation are outlined with respect to the different steps such as data partitioning, error calculation, statistical testing, and others. Further guidelines are also provided along selecting valid and suitable error measures depending on the specific characteristics of the dataset at hand.