1. Discontinuities Demo
Shrayes Ramesh, PhD.
Data Tactics Corporation
Thursday, November 14, 13

2. Challenges
• how do you decide which variables capture
what happened?
• /when/ did an event happen
• what's the effect of the event on the variables
• Can we construct a UI and algorithm to tackle
all three problem simultaneously?
Thursday, November 14, 13

3. Goals
• The goal is to feed in raw data as the sole
input, and obtain answers to all three
questions:
• (1) when did an event likely occur
• (2) what variables can we use to measure the
event
• (3) what was the effect of the event on those
variables
Thursday, November 14, 13

4. variable outcome
Simple example
effect
event
Thursday, November 14, 13
time

5. Scaled up
Thursday, November 14, 13

6. With limited insight…
• if we know timing and the important
variables, we can measure the effect of the
shock on the variables. (standard regression
techniques)
• if we know the set of important variables and
track variables over time, we can identify
timing of shocks.
• if we know timing and have a long history of
variable evolution, we can cluster variables by
their behavior at the important point in time
(relative to other points in time)
Thursday, November 14, 13

7. estimated effect
Methodology
correct
effect
correct timing
Thursday, November 14, 13

8. Methodology
•
For every time T and variable K, run an OLS, under the hypothesis that a shock
occurred at time T to variable K
•
Sample is restricted to variables for a neighborhood around t, i.e. [t-bandwith , t
+bandwidth]:
Y(K,t) = A(K,T) + B(K,T)S(t) + e(K,t) with
S(t) = 1(t>T) is an indicator with T as the time to test
•
Results are stored as the matrix of coefficients B(K,T)
•
OLS estimates of B(K,T) are biased towards zero to the extent that S(t) is
misspecified.
•
In other words, B(K,T) will be maximally different from zero (and unbiased) at the
true break T
Thursday, November 14, 13

9. Methodology
Thursday, November 14, 13

10. Answers
• when did an event likely occur?
– aggregate (sum) effects across all variables
• what variables can we use to measure the
event?
– which variables had the largest effect at time point?
• what was the effect of the event on those
variables?
– we just measured that
• what variables move together often across
time?
– show similar variables
Thursday, November 14, 13

11. Example 1: Super Bowl tweets
• Twitter streaming API (every tweet)
• Sample of data selected from Sunday,
February 3, 1600-2210 hours
• Binned into minute-by-minute word counts
• Out of 651k 1-grams, kept 1035 least sparse
(> 30% sparse) words.
• Input data is 371x1035 matrix
Thursday, November 14, 13

12. SUPERBOWL SHINY
Thursday, November 14, 13

13. Network graph of variables with
correlations > .95
Halftime show
Power outage
Thursday, November 14, 13

14. Deployable and Repeatable
• The model only requires data to be
transformed to a KxT matrix.
– K variables
– T time periods
We could use this model on many other data sets!
• minute-by-minute word count in twitter
• stock prices
• chatter on social media forums
Thursday, November 14, 13

15. Network graph of forums with
correlations > .27
Hezbollah
Thursday, November 14, 13

16. Future improvements
• OLS is simple and efficient, but other models
may be more accurate at estimating effects in
some cases
• exploring different approaches to choosing
which variables to consider and approach to
aggregating variable effects.
• massively parallel on all 630k words
simultaneously?
• real-time analytics on streaming data
Thursday, November 14, 13