HIGHLIGHTS
- What: The aim of this paper is to examine how and deep learning as well as technologies can be used to predict and detect anomalies including techniques such as the autoregressive integrated moving average model (ARIMA) K-nearest neighbor (KNN) algorithm and convolutional neural network (CNN). The aim of this model is to mine the common structure between data sets for cluster analysis or dimensionality reduction. The paper examines the growing trend of hybrid methodologies that is gaining more popularity.
- Who: Traffic flow prediction and collaborators from the School of Computer Science and Technology, Beijing . . .
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