Abstract:
Effectively evaluating the quality of pollution emission data from relevant enterprises is a significant technical problem. Based on time-series anomaly detection, IMI-TSA (Image Memory Induced Time-Series Anomaly), an image memory induced anomaly detection algorithm for atmospheric pollutant emission time-series data is proposed. Firstly, the mathematical definition of abnormal time-series is presented. Then, the image memory method is used to encode the time series and establish a memory mode based on the sequence data's image structure characteristics. Therefore, the category of unlabeled samples is obtained by the memory mode, using the correlation between features and categories of the samples. Finally, the image memory network is constructed with labeled and unlabeled samples to realize the time-series anomaly detection task. For ecological environmental protection, pollution discharge data from 8 representative enterprises are collected and anomaly detection for the data is conducted. The experiments show that the accuracy exceeds 80%, which means this algorithm can be used to build an atmospheric pollution data supervision platform.