Multivariate Time Series Classification: A Deep Learning Approach
Mohamed Abouelnaga Julien Vitay Aida Farahani
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.