Change Point Detection in Sequential Sensor Data using Recurrent Neural Networks
Abstract: Change-point detection is the problem of recognizing the abrupt variations in sequential data. This covers a wide range of real world problems within medical, meteorology and automotive industry, and has been actively addressed in the community of statistics and data mining. In the automotive industry, sequential data is collected from various components of the vehicles. The changes in the underlying distribution of the sequential data might indicate component failure, sensor degradation or different activity of the vehicle, which explains the need for detecting these deviations in this industry. The research question of this thesis focuses on how different architectures of the recurrent neural network (RNN) perform in detecting the change points of sequential sensor data. In this thesis, the sliding window method was utilised to represent the variable sequence length into fixed length. Then this fixed length sequences were provided to many input single output (MISO) and many input many output (MIMO) architectures of RNN to perform two different tasks such as sequence detection, where the position of the change point in the sequence is recognized and sequence classification, where the sequence is checked for the presence of a change point. The stacking ensemble technique was employed to combine results of sequence classification with the sequence detection to further enhance the performance. The result of the thesis shows that the MIMO architecture has higher precision than recall whereas MISO architecture has higher recall than precision but both having almost similar f1-score. The ensemble technique exhibit a boost in the performance of both the architectures.
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