  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.
