k-nearest neighbors algorithm

Physics

Geology

Aerospace engineering

Pattern recognition (psychology)

Pure mathematics

Dimension (graph theory)

Mathematics

Algorithm

Image (mathematics)

Nearest neighbor search

Similarity (geometry)

Data mining

Machine learning

Time series

Engineering

Anomaly (physics)

Sampling (signal processing)

Series (stratigraphy)

Paleontology

Artificial intelligence

Satellite

Set (abstract data type)

Programming language

Condensed matter physics

Computer science

Data set

Anomaly detection

Telecommunications

Detector

Chinese Journal of Aeronautics

In satellite anomaly detection, there are some problems such as unbalanced sample distribution, fewer fault samples, and unobvious anomaly characteristics. These problems cause the extisted anomaly detection methods are difficult to train accurate classification model, and the accuracy of anomaly detection is hard to improve. At the same time, the monitoring data of satellite has high dimension and is difficult to extract effective features. Based on the DTW over-sampling method, this paper realizes the over-sampling of fault samples in satellite time series, and constructs a distributed and balanced time series data set. The Fast-DTW method is applied to calculate the distance between different time series, which can improve the speed of similarity calculation. KNN (K-Nearest Neighbor) method is applied for classification and the best classification result is obtained by search the optimal hyper-parameters k. The results show that the proposed method has high anomaly detection accuracy and consumes short calculation time.

A method for satellite time series anomaly detection based on fast-DTW and improved-KNN