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STAPLE: Spatio-Temporal Precursor Learning for Event ForecastingYue Ning1 Rongrong Tao1 Chandan K. Reddy1 Huzefa Rangwala2 James C. Starz3 Naren Ramakrishnan1
1Discovery Analytics Center, Department of Computer Science, Virginia Tech.2Department of Computer Science, George Mason University.
3Lockheed Martin ATL.
Challlenges• Temporal ordering constraints on events.• Lack of class labels for precursor documents.• Data scarcity and imbalanced distribution in
certain geolocations.• Inadequacy of static features.
Introduction
Jan. 20, 2015: the National Assembly and the Senate Foreign Relations Committee had passed resolutions condemning the publication of the French magazine Jan. 23, 2015:
Jamaat-e-Islami (JI) Ameer Sirajul Haq on Friday asked the French government and the magazine to apologize
Jan. 24, 2015: Jamaatud Dawa (JuD) chief Hafiz Muhammad Saeed said the Muslims will launch a global movement against it
Jan. 27, 2015: Hundreds of students protested against a French magazine and stormed a school demanding it close
Bannu
Figure: Precursor event sequence discovered by our method fora protest event.
Societal event detection can be modeled as a systemof inter-connected locations, where each location isrecording a set of time-dependent observations. Inorder to detect event occurrence and automaticallyreconstruct the precursors and signals, it is essentialto model relationships between the di�erent loca-tions w.r.t. how events evolve over time.
1
Event Probability: 0.85
President PETA ……
Day 1 Day 2 Day 3 Day 4
Figure: Study influential factors such as name, location,organization etc.
1
San Francisco SeattleWashington D.C…… ……
Day 1 Day 4 Event Day
Figure: Spatio-Temporal indicators.
We develop a novel multi-task model with dynamicgraph constraints within a multi-instance learningframework. Our model tackles the problem ofscarce data distribution and reinforces co-occurringlocation-specific precursors with augmented repre-sentations. Through studies on civil unrest move-ments in numerous countries, we demonstrate thee�ectiveness of the proposed method for precursordiscovery and event forecasting.
Framework
1/28/2017 Trend Chart (Area + Line)
http://localhost:8080/ 1/1
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ews
protestAug 10
protestAug 22
protestSep 3
1/28/2017 Trend Chart (Area + Line)
http://localhost:8080/ 1/1
Aug 03 Aug 10 Aug 17 Aug 24 Aug 31 Sep 070
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ews
protestAug 3
protestAug 6
protestAug 10
protestAug 15
protestAug 19
protestAug 22
protestAug 25
protestAug 27
protestSep 3
Aug. 1
…...
Aug. 10
Event
Aug. 2
Aug. 1
…...
Aug. 10Aug. 2
Multi-Instance Learning
Spatio-Temporal Correlation Graph
Augmented Entity Embeddings
Org
Person
Org
Person
Location
Location
Event
Figure: Overall System Framework.
Key contributions of this paper• Dynamic graph constraints for precursor
learning and event forecasting• Augmented representation learning for
precursors• Multi-task learning for precursor mining• Comprehensive set of experiments in real-world
dataBy taking advantage of spatio-temporal event cor-relations within a multi-task learning framework,when compared to the best state-of-the-art algo-rithm• 86% of cities have a higher F1 score• 60% of cities have at least 20% improved F1 score
Formalisms
Objective function
min�
ÿ
kœK
Q
ccccca
Nk
NL(◊k) + ⁄1
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i
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lœGt
–ti
k,l
Q
ca◊k ≠ ◊lR
db2
+ ⁄2
2 ||◊ ≠ ◊k||22 + ⁄3
2 ||◊k||22R
dddddb (1)Here,• k, l are the indices for cities,• ◊k is the model parameter for city k,• ◊ represents the global model,• ti is the time index for the current event indicator,• ⁄1, ⁄2, ⁄3 are hyperparameters.• –
ti
k,l is the normalized weight on the edge betweencity k and city l at time ti, given by:
–ti
k,l=
Q
ccccca
ÿ
c
tiÿ
t=ti≠Hmin(Ek
t(c), E
l
t(c))
R
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1dist(k, l)
R
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(2)
Methods
1
Event: Labor Protest Location: D.C.
Time: 2015-04-01
Mar 20-23. 2015D.C
SFNYC
SEA
EDU
Labor D.C
EDU EDU
Labor
12
Mar 24-27. 2015 Mar 28-31. 2015
GOV1 1
1 2
2
21
D.C
SF
Seattle
NYC
D.C
D.C
NYC
NYC
NYC SEA
D.C
D.C
SF
SF
Figure: Spatio-temporal Correlation Graph.
Two factors influence the weight between two cities• Behavior Pattern: the number of similar events
that co-occurred in the past.• Geographical distance: two cities that are far
away from each other have fewercorrelations/similarities in their models
ABC
LK
time
AB
D
C
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(Y)� = (Y�Ymin)Ymax�Ymin
JT B GFBUVSF TDBMJOH GVODUJPO 1
Similar event patterns in the past, similar models
Closer geolocations, similar models
Event: Yes Location: A
Time: 2015-04-01
Results
0.4 0.5 0.6 0.7 0.8 0.9
1
Recall Precision F1VE
0.4 0.5 0.6 0.7 0.8 0.9
1
Recall Precision F1CO
0.4 0.5 0.6 0.7 0.8 0.9
1
Recall Precision F1PK
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
1 1.1
Recall Precision F1IR
0.4 0.5 0.6 0.7 0.8 0.9
1
Recall Precision F1AF
0.4 0.5 0.6 0.7 0.8 0.9
1 1.1
Recall Precision F1PY
0.4
0.6
0.8
1
1.2
1.4
Recall Precision F1AF
MISVM Relaxed Nested STAPLE-tx STAPLE
Figure: Prediction performance (Recall, Precision, and F1scores) on six datasets for the comparison methods
-0.5 0
0.5 1
1.5 2
2.5 3
3.5 4
0 100 200 300 400 500 600 700 800 900
F1 L
ift
#Events
(a) ICEWS
0
2
4
6
8
10
0 100 200 300 400 500 600
F1 L
ift
#Events
(b) GSR
Figure: F1 lift per city using STAPLE compared tostate-of-the-art model, nMIL. X-axis denotes the number ofevents at the city level. Y-axis denotes the F1 lift from theSTAPLE model.
Conclusion
STAPLE: a multi-task spatio-temporal correlationgraph model based on a two-level multi-instancelearning (MIL) framework for precursor mining cou-pled with event forecasting. Multiple models forcities are jointly learned together and proven to bee�ective at both forecasting events and discoveringprecursors.
References
[1] Yue Ning, Sathappan Muthiah, Huzefa Rangwala, andNaren Ramakrishnan.Modeling precursors for event forecasting via nestedmulti-instance learning.In KDD, pages 1095–1104, 2016.
[2] Zhi-Hua Zhou, Yu-Yin Sun, and Yu-Feng Li.Multi-instance learning by treating instances as non-i.i.d.samples.In ICML, pages 1249–1256, 2009.
[3] Liang Zhao, Feng Chen, Chang-Tien Lu, and NarenRamakrishnan.Spatiotemporal event forecasting in social media.In SDM, pages 963–971, 2015.
Acknowledgements
This work was partially funded by the U.S. Department ofHomeland Security under Grant Award Number 2017-ST-061-CINA01 and by the O�ce of Naval Research under contractN00014-16-C-1054.
Contact Information• Web: http://people.cs.vt.edu/yning• Email: [email protected]
