对比多种机器学习模型在时间序列预测中的表现，尤其关注长期预测和异常数据处理。
Addressing Challenges in Time Series Forecasting: A Comprehensive Comparison of Machine Learning Techniques
- 系统比较多种机器学习模型与传统ARIMA在时间序列回归中的效果。
- 在含异常值和缺失值的数据上，部分模型比ARIMA更稳定准确。
- 适合需要长期预测且数据质量不高的场景选择算法参考。
随着技术进步带来时间序列（TS）数据的爆炸式增长，现代管理系统越来越依赖对此类数据的分析，对高效处理方法的需求日益迫切。当前，用于时间序列分析与预测的先进机器学习（ML）方法正逐渐普及。本文简要描述并整理了适用于时间序列回归任务的合适算法，并在多个数据集上对这些算法进行对比，包括完整数据、含异常值的数据以及含缺失值的数据。重点评估其在长期预测中的准确性。本研究有助于根据预测需求和数据特征，选择最合适的算法。
原文摘要 · Abstract (English)
The explosion of Time Series (TS) data, driven by advancements in technology, necessitates sophisticated analytical methods. Modern management systems increasingly rely on analyzing this data, highlighting the importance of effcient processing techniques. State-of-the-art Machine Learning (ML) approaches for TS analysis and forecasting are becoming prevalent. This paper briefly describes and compiles suitable algorithms for TS regression task. We compare these algorithms against each other and the classic ARIMA method using diverse datasets: complete data, data with outliers, and data with missing values. The focus is on forecasting accuracy, particularly for long-term predictions. This research aids in selecting the most appropriate algorithm based on forecasting needs and data characteristics.
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。