Changepoints are points in a time-series surrounding which
the underlying time-series distribution changes significantly.
Quickest Change Detection
is the problem of declaring such changepoints with minimal delay
under maximum allowable probability of false alarm. HQCD
find changepoints for possibly inhomogeneous sources via a hierarchical
framework.
HQCD Overview:
Sources grouped into targets and surrogates
Targets: sources of importance
Surrogates: sources which can explain targets
Upper bounds probability of false alarm under the source-target
importance relationship - ML-PFA
Runs in real-time using specialized sequential Monte-Carlo
simulations
Infers causal connections between targets and surrogates via detected changepoints
Case Study: Brazil
Brazil witnessed its largest and protest during mid-2013 - often termed as
the Brazilian Spring.
We analyze Brazilian spring using HQCD (and compare against several
state-of-the art methods)
to identify the tipping point in the protest timeline by simultaneously
modeling the different protest types and protest related chatter in Twitter.
Total Protests
Major Events leading to Mass protests
Jun 21-23 :
Brazilian Spring saw around 250,000 protesters on Jun 17
HQCD found Jun 16th as the changepoint in total protests
Changepoint in Total ProtestsTimeNumber of protestsBOCPDGLRTW-GLRTRuLSIFHQCD
Protests by Categories
Total protests aggregates different kinds of protests which masks several
key events. HQCD finds those events by categories below.
Major Energy and Resources related events leading to Mass protests
Jun 02
Protest against ban of vans which were used as alternate means of
transport
Jun 02 :
Evacuation of landless people in Sao Paulo
HQCD declares significant change in this category to be Jun 02
Feb 2013Mar 2013Apr 2013May 2013Jun 2013Jul 2013Aug 2013Sep 2013020406080Changepoint in Energy and ResourcesTimeNumber of protestsBOCPDGLRTW-GLRTRuLSIFHQCD
Major Housing related events leading to Mass protests
Jun 14 :
Protests for affordable housing near the National Stadium at the Capital
Jun 14 :
Protests against World Cup questioning evacuation of residents
HQCD declares significant change in this category to be Jun 16
Feb 2013Mar 2013Apr 2013May 2013Jun 2013Jul 2013Aug 2013Sep 2013012345Changepoint in HousingTimeNumber of protestsBOCPDGLRTW-GLRTRuLSIFHQCD
Major Govt. related events leading to Mass protests
Jun 15 :
Protests against police violance in the state of Sao Paulo
HQCD declares significant change in this category to be Jun 16
Feb 2013Mar 2013Apr 2013May 2013Jun 2013Jul 2013Aug 2013Sep 20130510152025Changepoint in Other GovernmentTimeNumber of protestsBOCPDGLRTW-GLRTRuLSIFHQCD
Major Other related events leading to Mass protests
Jun 21 :
Violent protests in cities that hosted the Confederation cup
HQCD declares significant change in this category to be Jun 23
Feb 2013Mar 2013Apr 2013May 2013Jun 2013Jul 2013Aug 2013Sep 2013010203040Changepoint in OtherTimeNumber of protestsBOCPDGLRTW-GLRTRuLSIFHQCD
Major Economic related events leading to Mass protests
Jun 27 :
Student protests demanding better allocation of public funding towards education
Jun 29 :
Protest marches demanding better GDP investment policies
HQCD declares significant change in this category to be Jun 30
Feb 2013Mar 2013Apr 2013May 2013Jun 2013Jul 2013Aug 2013Sep 2013051015Changepoint in Other EconomicTimeNumber of protestsBOCPDGLRTW-GLRTRuLSIFHQCD
Major Employment related events leading to Mass protests
Aug 16 :
Teachers and professionals demanding increase in wages
Aug 18 :
Protests against ultimatum set by the municipality w.r.t. wage protests
HQCD declares significant change in this category to be Aug 18
Feb 2013Mar 2013Apr 2013May 2013Jun 2013Jul 2013Aug 2013Sep 20130102030Changepoint in EmploymentTimeNumber of protestsBOCPDGLRTW-GLRTRuLSIFHQCD
Explaining Brazilian Spring
Significant changes for different protest categories occurred at different
timepoints. We are interested in analysing how changes in these categories
influence each other.
HQCD provides a changepoint correlation matrix for target-target
and target-surrogate as below. Through these matrices we can find the interaction
strengths.
EmploymentEnergyHousingOtherEconomicGovernmentEmploymentEnergyHousingOtherEconomicGovernmentChangepoint through Protests10152025303540
cluster-00cluster-01cluster-02cluster-03cluster-04cluster-05cluster-06cluster-07cluster-08cluster-09cluster-10cluster-11cluster-12cluster-13cluster-14cluster-15cluster-16cluster-17cluster-18cluster-19cluster-20cluster-21cluster-22cluster-23cluster-24cluster-25cluster-26cluster-27cluster-28cluster-29EmploymentEnergyHousingOtherEconomicGovernmentChangepoint through Twitter5101520253035
Analysis
Other Economic and Employment were affected by Housing related protests.
Although, total number of housing protests were much smaller compared to
the other categories.
Housing and employment shares influences from similar Twitter
chatter clusters(cluster 01 and 26).
- Strong interaction came from social sphere
Housing and Other Economic are weakly related through Twitter
chatter clusters
- interactions manifested via category relation only.