Multivariate Time Series Classification: A Deep Learning Approach
This work addresses event detection in sensor data for applications like smart environments, but it is incremental as it applies existing methods to a new dataset without major innovations.
The paper tackled multivariate time series classification using gas sensor data to detect events like occupancy, comparing Fully Convolutional Networks, LSTM, and Recurrent Autoencoders, with results evaluated based on precision and recall metrics.
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.