MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Timeseries Forecasting and Prediction

Iman Deznabi, Madalina Fiterau

Keywords: Time series, Wavelets, Wavelet decomposition, Recurrent Neural Networks, Deep Learning

TL;DR: In multivariate time-series datasets changes in signals occurs in different frequencies. MultiWave decomposes signals into different frequencies removes the irrelevant frequencies and models each group using a model component.

Abstract: One of the challenges in multivariate time series modeling is that changes in signals occur with different frequencies, even when the sampling rate is consistent across signals. In the case of multivariate time series prediction, the outcome is also determined by patterns of different frequencies. These encapsulate both long-term and short-term effects, which have so far not been sufficiently leveraged by deep learning time series models. We fill this gap by introducing a framework, called MultiWave, which augments any deep learning time series model with components operating at the intrinsic frequencies of the signals. MultiWave applies wavelet decomposition on each signal to obtain subsignals of different frequencies and groups all subsignals in the same frequency band together to train a component. The output of the components is combined through a gating mechanism that removes irrelevant frequencies for the given predictive task. We show that MultiWave accurately determines the informative frequency bands and that the augmented models including components trained to operate on those bands outperform the original models. We further show that applying MultiWave on top of different deep learning models improves their performance in several real-world applications.

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Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/multiwave-multiresolution-deep-architectures/code)