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.

翻译：时间序列分类的研究已取得长足进步，从1NN-DTW算法到ROCKET系列分类器，技术不断演进。然而，在当前新型分类器快速迭代的背景下，退一步进行简单的基线检验至关重要。由于研究者致力于创造新最优结果、开发可扩展算法并实现模型可解释性，这类检验常被忽视。实际上，许多数据集初看具有时间序列特征，但经典算法（如忽视时间顺序的表格化方法）可能在这些问题上表现更优。例如，对于光谱数据集，表格化方法通常显著优于近期时间序列方法。本研究将采用经典机器学习方法（如Ridge、LDA、RandomForest）的表格化模型与ROCKET系列分类器（如Rocket、MiniRocket、MultiRocket）进行性能比较。表格化模型简洁高效，而ROCKET系列分类器结构更复杂，在近期时间序列分类器中兼具顶尖的准确率与效率。实验表明，在UCR/UEA基准测试中，表格化模型在约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%的数据集上实现精度差距在10个百分点以内的结果。研究提示，开发时间序列分类器时应将简单表格化模型纳入基线考量。此类模型运算极快，其有效性可比肩复杂方法，且更易于理解与部署。