HQCD

HQCD is a hierarchical framework to detect changepoints accross weakly-correlated and possibily inhomogenous sources via a hierarchical setup. Typically, we classify the sources into two groups:

1. Targets: sources which are of prime importance
2. Surrogates: sources which can explain targets but explaining these sources are of not prime importance.

A walkthrough demo of HQCD as applied on the Brazilian Spring can be seen at https://prithwi.github.io/hqcd_supplementary.

Datasets:

The processed datasets used in tha accompaniment paper are given below.

Synthetic data:

These sources were simulated using known changepoints and changepoints detected by various algorithms were compared against the same.

• True Changepoints
• Target Level data
• Surrogate Level data

Real World Example: Protests

Processed protest counts for 3 countries are also provided. Geolocated Twitter chatter, more specifically intensity of keywords for the country of interest over time, were used as surrogates. Typically, individual keywords are noisy and hence we clustered the keywords into 30 groups for each country. The processed Twitter cluster counts as well as the keywords belonging to the clusters are given below:

• Brazil

  • Protest counts (target)
  • Twitter Cluster counts (surrogate)
  • Mapping of twitter keywords to clusters

• Uruguay

  • Protest counts (target)
  • Twitter Cluster counts (surrogate)
  • Mapping of twitter keywords to clusters

• Venezuela

  • Protest counts (target)
  • Twitter Cluster counts (surrogate)
  • Mapping of twitter keywords to clusters