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.

翻译：本文探讨了适用于时间序列分类领域的多种方法及不同的神经网络架构。数据来源于一组气体传感器，这些传感器测量并追踪氧气、声音等物理量。借助这些数据，我们可以检测特定环境中的占用事件。首先，我们分析时间序列数据，以理解不同参数（如序列长度）对模型训练的影响。这些模型采用全卷积网络（FCN）和长短期记忆网络（LSTM）进行监督学习，并使用循环自编码器进行半监督学习。在整个研究过程中，我们基于精确率和召回率等指标，识别这些方法之间的差异，以确定最适合该问题的技术方案。