Time series classification is an important problem in real world. Due to its non-stationary property that the distribution changes over time, it remains challenging to build models for generalization to unseen distributions. In this paper, we propose to view the time series classification problem from the distribution perspective. We argue that the temporal complexity attributes to the unknown latent distributions within. To this end, we propose DIVERSIFY to learn generalized representations for time series classification. DIVERSIFY takes an iterative process: it first obtains the worst-case distribution scenario via adversarial training, then matches the distributions of the obtained sub-domains. We also present some theoretical insights. We conduct experiments on gesture recognition, speech commands recognition, wearable stress and affect detection, and sensor-based human activity recognition with a total of seven datasets in different settings. Results demonstrate that DIVERSIFY significantly outperforms other baselines and effectively characterizes the latent distributions by qualitative and quantitative analysis. Code is available at: https://github.com/microsoft/robustlearn.

翻译：时间序列分类是现实世界中的一个重要问题。由于其非平稳性导致分布随时间变化，构建能够泛化到未见分布的模型仍具挑战性。在本文中，我们提出从分布视角审视时间序列分类问题。我们认为时间复杂性源于其中未知的潜在分布。为此，我们提出DIVERSIFY方法以学习时间序列分类的泛化表示。DIVERSIFY采用迭代过程：首先通过对抗训练获取最坏情况下的分布场景，随后对齐所得子域的分布。我们还提供了若干理论见解。我们在手势识别、语音指令识别、可穿戴压力与情感检测以及基于传感器的人类活动识别等七种不同设置的数据集上进行了实验。结果表明，DIVERSIFY显著优于其他基线方法，并通过定性与定量分析有效刻画了潜在分布。代码详见：https://github.com/microsoft/robustlearn。