data science popup austin: surfing silver dynamic bayesian forecasting for fun and profit

DATA SCIENCEPOP UP

AUSTIN

Surfing Silver: Dynamic Bayesian Forecasting for Fun and Profit

Jonathan DinuAuthor and Teacher

clearspandex

DATA SCIENCEPOP UP

AUSTIN

#datapopupaustin

April 13, 2016Galvanize, Austin Campus

http://www.dominodatalab.com

SURFING SILVERDYNAMIC BAYESIAN FORECASTING FOR FUN AND PROFIT

Jonathan Dinu // April 13th, 2016 // @clearspandex

whoami


THE 2008 ELECTION

let me tell you a little story...


SPOILER ALERT...IT'S BEEN DONE BEFORE


> Nate Silver> Drew Linzer> Josh Putnam

> Simon Jackman


ANDREW GELMAN


ANDREW GELMAN (1995...)


THE THEORY BEHIND THE MAGIC

Courtesy of 538 and Drew Linzer (Votamatic)


http://fivethirtyeight.com/features/how-we-are-forecasting-the-2016-presidential-primary-election/

http://polmeth.wustl.edu/files/polmeth/linzerprespollmay12.pdf

CHALLENGES

> Historical Predictions susceptible to Uncertainty

> Sparse pre-election Poll Data

> Sampling Error and House Effects Bias Polls


WHAT DREW (AND NATE) DID DIFFERENTLY

> State level vs. National Polls

> Online Updates as more data become available> Not All Polls are Created Equal (weights/averages)> (Probabilistic) Forecasting in addition to Estimation


DYNAMIC BAYESIAN FORECASTING2

National: State:

Forecasts:

Not shown here: informative priors based on historical predictions


SO WHY AM I TELLING YOU THIS THEN?


STRUCTURED PREDICTIONSUPERVISED LEARNING ON SEQUENCES


TRADITIONALLY


STATES + TIME + TRANSITIONS


GRAPHICAL MODELS

> Assess Risk (uncertainty) as Probability of Failure

> Unobservable (hidden) Failure States

> Proactive/Early Prediction> Interpretable Latent Properties

> Online Algorithm (iteratively improve)


KEY IDEAS

> Uncertainty> Point vs. Distribution (or confidence intervals)

> Bayesian vs. Frequentists methods> Temporal variability

All models are wrong, but some models are useful... or something


KEY IDEAS (APPLIED)


IOT IMPACT: DETECTING MACHINE FAILURES

> Historical Structural Predictions susceptible to Uncertainty(Supervised Learning)

> Sparse pre-election Poll Data (costly to measure)> Sampling Error Biases Polls Inspections

(prediction in the absence of data)> Online Updates as more data become available

> Not All Polls sensors are Created Equal (weights/averages)> (Probabilistic) Forecasting in addition to Estimation


REMEMBER THIS...

National: State:

Forecasts:


INDUSTRIAL MACHINES3

HTTP://WWW.CITEMASTER.NET/GET/8BD1ACC0-F04B-11E3-BBAF-00163E009CC7/SALFNER05PREDICTING.PDFJonathan Dinu // April 13th, 2016 // @clearspandex

MORE INTERPRETABLEWE HAVE TO ACTUALLY FIX THE MACHINES AFTER ALL...


LATENT FACTORS


CAUSALITY!


REFERENCES

> The Signal and the Noise> Data Journalism Handbook

> Dynamic Bayesian Forecasting of Presidential Elections in the States (Drew A. Linzer)

> Time for Change model (Alan Abramowitz)> Baysian Data Analysis Gelman

> Causality Judea Pearl> 538: How we are Forecasting the 2016 Primaries

> Predicting Time-to-Failure of Industrial Machines with Temporal Data MiningJonathan Dinu // April 13th, 2016 // @clearspandex

DATA SCIENCEPOP UP

AUSTIN

@datapopup #datapopupaustin

data science popup austin: surfing silver dynamic bayesian forecasting for fun and profit

Data & Analytics