PP: Extracting statisticla graph features for accurate and efficient time series classification
Problem: TSC, time series classification;
Traditional TSC: find global similarities or local patterns/subsequence(shapelet).
We extract statistical features from VG to facilitate TSC
Introduction:
Global similarity:
the difference between TSC and other classification: deal with sequentiality property.
traditional methods: K-NN algorithm + DTW, one intrinsic issue with DTW, is that it focuses on finding global similarities. 在我看来这句话,简直是boo shit,一个距离测量只关注与全局的相似度?它应该是全部的距离都包含。
Local features:
Bag-of-patterns; SAX-VSM; shapelets-based algorithms.
Suffering:
- high computation complexity
- suboptimal classification accuracy
Time series --------> VG --------> graph features
graph features: Motif distribution, density;
Q:
- why it's called multiscale VG
- the statistical graph features: probability distributions of small motifs, assortativity and degree statistics.
much faster than Learning Shapelets and Fast Shapelet.
Future work:
1. Other useful and efficient graph features: degree distribution entropy, centrality, bipartivity, etc.
2. adopt MVG for multivariate TSC.
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