回归基础：对现代时间序列分类算法的合理性检查
Back to Basics: A Sanity Check on Modern Time Series Classification Algorithms
AI总结：
本文通过将Ridge、LDA和RandomForest等经典表格模型与ROCKET系列时间序列分类器在UCR/UEA基准上比较，证明简单表格基线在部分数据集上更优，应纳入时间序列分类评估。
摘要：
时间序列分类的最先进技术已经取得了长足发展，从1NN-DTW算法发展到ROCKET系列分类器。然而，在当前新分类器快速发展的过程中，退一步进行简单的基线检查至关重要。这些检查往往被忽视，因为研究人员专注于建立新的最先进结果、开发可扩展算法以及使模型具有可解释性。尽管如此，仍有许多数据集乍看像时间序列，但在这类问题上，不考虑时间顺序的表格方法等经典算法可能表现更好。例如，对于光谱数据集，表格方法往往显著优于近期的时间序列方法。在本研究中，我们将使用经典机器学习方法（如Ridge、LDA、RandomForest）的表格模型与ROCKET系列分类器（如Rocket、MiniRocket、MultiRocket）的性能进行比较。表格模型简单且非常高效，而ROCKET系列分类器更复杂，并在近期的时间序列分类器中具有最先进的准确率和效率。我们发现，在UCR/UEA基准测试中，表格模型在约19%的单变量数据集和28%的多变量数据集上优于ROCKET系列分类器，并在约50%的数据集上取得了相差不超过10个百分点的准确率。我们的结果表明，在开发时间序列分类器时，将简单表格模型视为基线非常重要。这些模型速度很快，可以与更复杂的方法同样有效，并且可能更易于理解和部署。
英文摘要：
The state-of-the-art in time series classification has come a long way, from the 1NN-DTW algorithm to the ROCKET family of classifiers. However, in the current fast-paced development of new classifiers, taking a step back and performing simple baseline checks is essential. These checks are often overlooked, as researchers are focused on establishing new state-of-the-art results, developing scalable algorithms, and making models explainable. Nevertheless, there are many datasets that look like time series at first glance, but classic algorithms such as tabular methods with no time ordering may perform better on such problems. For example, for spectroscopy datasets, tabular methods tend to significantly outperform recent time series methods. In this study, we compare the performance of tabular models using classic machine learning approaches (e.g., Ridge, LDA, RandomForest) with the ROCKET family of classifiers (e.g., Rocket, MiniRocket, MultiRocket). Tabular models are simple and very efficient, while the ROCKET family of classifiers are more complex and have state-of-the-art accuracy and efficiency among recent time series classifiers. We find that tabular models outperform the ROCKET family of classifiers on approximately 19% of univariate and 28% of multivariate datasets in the UCR/UEA benchmark and achieve accuracy within 10 percentage points on about 50% of datasets. Our results suggest that it is important to consider simple tabular models as baselines when developing time series classifiers. These models are very fast, can be as effective as more complex methods and may be easier to understand and deploy.
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。
