Big Data, Innovations and New Business Models Predicting Human Behaviour with Big Data
Tobias Preis Warwick Business School
@t_preis [email protected] Our team
Tobias Preis Steven Bishop
Philip Treleaven Suzy Moat Nick Chater A map of the world built only from GPS locations of Flickr photos Preis & Moat (2013)
Internet data opens up opportunities for new dynamic, adaptive economic models Data Revolution OutperformanceGoogle Trading Strategy for Keyword “debt”
Buy and Hold Strategy Google Trends Strategy 300 μ+σ, μ, μ−σ of Random Strategy 326 200 ‘debt’ 100 0 16 2004 2005 2006 2007 2008 2009 2010 2011 Pro f t and Loss [%] Time, t [Years]
Preis, Moat & Stanley, Scientifc Reports 3, 1684 (2013) http://www.nature.com/srep/2013/130425/srep01684/full/srep01684.html
Covered by QuantifyingWikipedia: Patterns Financial topics 1.00
Wikipedia Edits 0.75 Financial Topics Wikipedia Views Financial Topics 0.50 Density Random 0.25 Strategy Views strategies proftable 0.00 −2 0 2 Return [Std. Dev. of Random Strategies]
Moat, Curme, Avakian, Kenett, Stanley & Preis, Scientifc Reports 3, 1801 (2013) http://www.nature.com/srep/2013/130508/srep01801/full/srep01801.html
Covered by QuantifyingHourly Flickr Patternsdata and Hurricane Sandy 0.10 A 0.08 0.06 Landfall of Hurricane Sandy 0.04 Flickr Photos with Hurricane Related Text 0.02 0.00 20 Oct 25 Oct 30 Oct 05 Nov 10 Nov 15 Nov 20 Nov Normalised Number of Photos Time
B Landfall of Hurricane Sandy 1020 1000 Averaged Pressure in 980 US State New Jersey 960
20 Oct 25 Oct 30 Oct 05 Nov 10 Nov 15 Nov 20 Nov
Atmospheric Pressure [mbar] Time Preis, Moat, Bishop, Treleaven & Stanley, Scientifc Reports 3, 3141 (2013) http://www.nature.com/srep/2013/131105/srep03141/full/srep03141.html
Covered by What Google Knows About Houses House Price Forecast based on Google Trends Data Outperformance Training Period Out-of-Sample Forecast 190 ● ●● ●● ● ● ● ●● ● ●● ● 180 ● ●● ● ● ●●● ● ● ● ● ●● ● ● ●● ● ● ●● ● ●● ● ● ● ●● x ● ● ● ●● ● ● ●● ● ● ●● e ● 170 ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ●
ice Ind ● ●
r ● 160 ● ● ● ●● ● ● ●●● ●●● ● ● ● ●● ●● Predicted Value 150 ●● ●● ● ● Observed Value House P ● ● 80% Prediction Interval 140 ● 95% Prediction Interval ● ●●
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Preis, Bishop, Stanley & Moat, Under Review House Price Forecast based on Google Trends Data
Outperformance Baseline Advanced IN-SAMPLE FORECAST MAE 1.48 1.37 4 RMSE 1.89 1.75
2
0
−2 orecast Error [%] F 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Baseline Advanced OUT-OF-SAMPLE FORECAST MAE 1.74 1.66 4 RMSE 2.09 1.99
2
0
−2 orecast Error [%] F 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Time [Months] Preis, Bishop, Stanley & Moat, Under Review Summary Home Summary 1. Large technological systems have become a central part of our society.
2. These systems form a world wide sensor network, measuring human behaviour on a massive scale.
3. We can use this data to: • measure what is happening in our world right now • anticipate what will happen in our world later