Multivariate Time Series Classification: A Deep Learning Approach

TL;DR

This study compares various neural network architectures like FCN, LSTM, and Recurrent Autoencoders for classifying multivariate time series data from gas sensors, focusing on their effectiveness in detecting environmental events.

Contribution

It provides an analysis of different deep learning models for multivariate time series classification and evaluates their performance on sensor data for event detection.

Findings

FCN and LSTM outperform Autoencoders in classification accuracy

Sequence length significantly affects model performance

Recurrent Autoencoders are effective for semi-supervised learning

Abstract

This paper investigates different methods and various neural network architectures applicable in the time series classification domain. The data is obtained from a fleet of gas sensors that measure and track quantities such as oxygen and sound. With the help of this data, we can detect events such as occupancy in a specific environment. At first, we analyze the time series data to understand the effect of different parameters, such as the sequence length, when training our models. These models employ Fully Convolutional Networks (FCN) and Long Short-Term Memory (LSTM) for supervised learning and Recurrent Autoencoders for semisupervised learning. Throughout this study, we spot the differences between these methods based on metrics such as precision and recall identifying which technique best suits this problem.