Wavelet Probabilistic Recurrent Convolutional Network for Multivariate Time Series Classification

TL;DR

This paper introduces a Wavelet Probabilistic Recurrent Convolutional Network (WPRCN) that effectively classifies multivariate time series by handling non-stationarity, noise, and data scarcity through a novel probabilistic wavelet module integrated with neural networks.

Contribution

The paper proposes a versatile wavelet probabilistic module that enhances deep neural networks for multivariate time series classification, especially under challenging conditions.

Findings

Outperforms benchmark algorithms on 30 diverse datasets.

Excels in scenarios with scarce data and non-stationary signals.

Demonstrates broad applicability with LSTM and C-FCN architectures.

Abstract

This paper presents a Wavelet Probabilistic Recurrent Convolutional Network (WPRCN) for Multivariate Time Series Classification (MTSC), especially effective in handling non-stationary environments, data scarcity and noise perturbations. We introduce a versatile wavelet probabilistic module designed to extract and analyse the probabilistic features, which can seamlessly integrate with a variety of neural network architectures. This probabilistic module comprises an Adaptive Wavelet Probabilistic Feature Generator (AWPG) and a Channel Attention-based Probabilistic Temporal Convolutional Network (APTCN). Such formulation extends the application of wavelet probabilistic neural networks to deep neural networks for MTSC. The AWPG constructs an ensemble probabilistic model addressing different data scarcities and non-stationarity; it adaptively selects the optimal ones and generates probabilistic features for APTCN. The APTCN analyses the correlations of the features and forms a comprehensive feature space with existing MTSC models for classification. Here, we instantiate the proposed module to work in parallel with a Long Short-Term Memory (LSTM) network and a Causal Fully Convolutional Network (C-FCN), demonstrating its broad applicability in time series analysis. The WPRCN is evaluated on 30 diverse MTS datasets and outperforms all the benchmark algorithms on average accuracy and rank, exhibiting pronounced strength in handling scarce data and physiological data subject to perturbations and non-stationarities.