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.

本研究探究适用于时间序列分类领域的不同方法和各种神经网络结构。该论文通过使用来自气体传感器的数据来探测某一特定环境中的占用状态，并使用 Fully Convolutional Networks (FCN) 和 Long Short-Term Memory (LSTM) 进行监督学习和递归自编码器进行半监督学习，通过评估精度和召回率等指标来确定哪种方法最适合此问题。