论文标题
使用SKTIME进行预测：设计SKTITE的新预测API并应用其复制和扩展M4研究
Forecasting with sktime: Designing sktime's New Forecasting API and Applying It to Replicate and Extend the M4 Study
论文作者
论文摘要
我们提出了一个新的开源框架，用于在Python中进行预测。我们的框架构成了SKTime的一部分，Sktime是一个更通用的机器学习工具箱，用于时间序列，具有Scikit-Learn兼容接口，用于不同的学习任务。我们的新框架提供了专门的预测算法和工具来构建，调整和评估复合模型。我们使用SKTIME复制并扩展了M4预测研究的关键结果。特别是，我们进一步研究了单变量预测的简单现成机器学习方法的潜力。我们的主要结果是，简单的混合方法可以提高统计模型的性能，而简单的纯种方法可以在每小时数据集上实现竞争性能，超越统计算法并接近M4获奖者。
We present a new open-source framework for forecasting in Python. Our framework forms part of sktime, a more general machine learning toolbox for time series with scikit-learn compatible interfaces for different learning tasks. Our new framework provides dedicated forecasting algorithms and tools to build, tune and evaluate composite models. We use sktime to both replicate and extend key results from the M4 forecasting study. In particular, we further investigate the potential of simple off-the-shelf machine learning approaches for univariate forecasting. Our main results are that simple hybrid approaches can boost the performance of statistical models, and that simple pure approaches can achieve competitive performance on the hourly data set, outperforming the statistical algorithms and coming close to the M4 winner.
