PP: Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

From: Stanford University; Jure Leskovec, citation 6w+; 

Problem:

subsequence clustering.

Challenging:

discover patterns is challenging because it requires simultaneous segmentation and clustering of the time series + interpreting the cluster results is difficult. 

Why discover time series patterns is a challenge?? thinking by yourself!! there are already so many distance measures(DTW, manifold distance) and clustering methods(knn,k-means etc.). But I admit the interpretation is difficult.

Introduction:

long time series ----breakdown-----> a sequence of states/patterns ------> so time series can be expressed as a sequential timeline of a few key states. -------> discover repeated patterns/ understand trends/ detect anomalies/ better interpret large and high-dimensional datasets. 

Key steps: simultaneously segment and cluster the time series.

Unsupervised learning: hard to interpretation, after clustering, you have to view data itself.

how to discover interpretable structure in the data?

distance-based metrics, DTW.

Reference: 

1. 如何用简单易懂的例子解释条件随机场（CRF）模型？

PP: Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

原文：https://www.cnblogs.com/dulun/p/12244506.html