The analysis of multivariate time series data is challenging due to the various frequencies of signal changes that can occur over both short and long terms. Furthermore, standard deep learning models are often unsuitable for such datasets, as signals are typically sampled at different rates. To address these issues, we introduce MultiWave, a novel framework that enhances deep learning time series models by incorporating components that operate at the intrinsic frequencies of signals. MultiWave uses wavelets to decompose each signal into subsignals of varying frequencies and groups them into frequency bands. Each frequency band is handled by a different component of our model. A gating mechanism combines the output of the components to produce sparse models that use only specific signals at specific frequencies. Our experiments demonstrate that MultiWave accurately identifies informative frequency bands and improves the performance of various deep learning models, including LSTM, Transformer, and CNN-based models, for a wide range of applications. It attains top performance in stress and affect detection from wearables. It also increases the AUC of the best-performing model by 5% for in-hospital COVID-19 mortality prediction from patient blood samples and for human activity recognition from accelerometer and gyroscope data. We show that MultiWave consistently identifies critical features and their frequency components, thus providing valuable insights into the applications studied.

翻译：多变量时间序列数据分析因信号在短期和长期内存在多种频率变化而极具挑战性。此外，标准深度学习模型往往不适用于此类数据集，因为信号通常以不同速率采样。为解决这些问题，我们提出MultiWave——一种新颖框架，通过嵌入基于信号固有频率运行的组件来增强深度学习时间序列模型。MultiWave利用小波将每个信号分解为不同频率的子信号，并将其分组为频带。每个频带由模型的不同组件处理。门控机制整合各组件的输出，生成仅使用特定频率下特定信号的稀疏模型。实验表明，MultiWave能够准确识别信息量丰富的频带，并提升LSTM、Transformer及基于CNN的模型等多种深度学习模型在广泛应用场景中的性能。它在可穿戴设备压力与情绪检测中达到最优性能。同时，在基于患者血液样本的院内COVID-19死亡率预测和基于加速度计与陀螺仪数据的人体活动识别任务中，它将最优模型的AUC提升了5%。我们证明MultiWave能持续识别关键特征及其频率分量，从而为所研究的应用提供有价值的洞见。