时间序列预测能否被自动化？一个基准与解析
Can time series forecasting be automated? A benchmark and analysis
AI总结：
针对时间序列预测方法选择困难的问题，提出综合基准评估AutoGluon-Timeseries和sktime框架，揭示其适用性并促进最优预测方法选择。
摘要：
在机器学习与人工智能领域，时间序列预测在金融、医疗和气象等多个领域发挥着关键作用。然而，由于数据模式与特征的多样性，为给定数据集选择最合适的预测方法是一项复杂的任务。本研究旨在通过提出一个综合基准来解决这一挑战，该基准用于在广泛的数据集上评估和排名时间序列预测方法。本研究调查了来自两个著名时间序列预测框架 AutoGluon-Timeseries 和 sktime 的多种方法的比较性能，以揭示它们在不同现实场景中的适用性。本研究通过提供稳健的基准测试方法，并促进在为实现最优预测而选择预测方法时的明智决策，为时间序列预测领域做出了贡献。
英文摘要：
In the field of machine learning and artificial intelligence, time series forecasting plays a pivotal role across various domains such as finance, healthcare, and weather. However, the task of selecting the most suitable forecasting method for a given dataset is a complex task due to the diversity of data patterns and characteristics. This research aims to address this challenge by proposing a comprehensive benchmark for evaluating and ranking time series forecasting methods across a wide range of datasets. This study investigates the comparative performance of many methods from two prominent time series forecasting frameworks, AutoGluon-Timeseries, and sktime to shed light on their applicability in different real-world scenarios. This research contributes to the field of time series forecasting by providing a robust benchmarking methodology and facilitating informed decision-making when choosing forecasting methods for achieving optimal prediction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
