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…

Problem: ?? mining relationships in time series data; A new class of relationships in time series data. traditional methods: discover pair-wise relationships. Introduction: Challenge: discovery of complex patterns of relationships between individual…

图Lasso求逆协方差矩阵(Graphical Lasso for inverse covariance matrix) 作者:凯鲁嘎吉 - 博客园 http://www.cnblogs.com/kailugaji/ 1. 图Lasso方法的基本理论 2. 坐标下降算法 3. 图Lasso算法 4. MATLAB程序 数据见参考文献[2] 4.1 方法一 demo.m load SP500 data = normlization(data); S = cov(data); %样本协方差 [X,…

PROBLEM: OmniAnomaly multivariate time series anomaly detection + unsupervised 主体思想: input: multivariate time series to RNN ------> capture the normal patterns -----> reconstruct input data by the representations ------> use the reconstruction pr…

from: Dacheng Tao 悉尼大学 PROBLEM: time series retrieval: given the current multivariate time series segment, how to obtain its relevant time series segments in the historical data. Two challenging: 1. it requires a compact representation of the raw tim…

Problem: unsupervised clustering represent data in feature space; learn a non-linear mapping from data space X to feature space Z. Problem formulation: cluster a set of n points into k clusters, each represented by a centroid uj. Instead of clusterin…

1. Air Pollution Forecasting In this tutorial, we are going to use the Air Quality dataset. This is a dataset that reports on the weather and the level of pollution each hour for five years at the US embassy in Beijing, China. The data includes the d…

Problem: time series prediction The nonlinear autoregressive exogenous model: The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values…

Problem: clustering A clustering network transforms the data into another space and then selects one of the clusters. Next, the autoencoder associated with this cluster is used to reconstruct the data-point. Introduction: traditional method: data----…

Improvement can be done in fulture:1. the algorithm of constructing network from distance matrix. 2. evolution of sliding time window3. the later processing or visual analysis of generated graphs. Thinking: 1.What's the ground truth in load profiles?…