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ECIG 2007 Modeling www time series

ECIG 2007 Modeling www time series. The research opportunity A word on time series models Data Models Results What have we learned? Next steps. Research opportunity. CSR: Organizations manage a widening set of stakeholders Power of exit Power of voice

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ECIG 2007 Modeling www time series

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  1. ECIG 2007Modeling www time series • The research opportunity • A word on time series models • Data • Models • Results • What have we learned? • Next steps 2007 Stéphane Gauvin FSA - ULaval

  2. Research opportunity • CSR: Organizations manage a widening set of stakeholders • Power of exit • Power of voice • The digital sphere has become the Übermedia • Voices are innumerable • Which voice will become dominant? (eg: anti-smoking, fat lawsuits, vegetarianism) • General question is: • Can we measure and forecast real-world opinions merely by listening to the digital sphere? • Today’s question is: • How strong is the signal in the digital sphere? 2007 Stéphane Gauvin FSA - ULaval

  3. A word on timeseries models • Marketing is concerned with theory building • Data mining is atheoretical • Trends are as a nuisance • First step is to take first and second differences • VAR and/or co-integration • Dekimpe & Hanssens IJRM 2000, WP 2006 • Franses JMR 2005 2007 Stéphane Gauvin FSA - ULaval

  4. Into the looking glass • The digital sphere is invisible. It is queried (googled) • We all google all the time to retrieve specific instances • Swammer searches to count instances 2007 Stéphane Gauvin FSA - ULaval

  5. Swammer • Build an intelligent set of queries to compute index • Shown to be close to survey data 2007 Stéphane Gauvin FSA - ULaval

  6. Illustrative data 2007 Stéphane Gauvin FSA - ULaval

  7. Robust or else 2007 Stéphane Gauvin FSA - ULaval

  8. Storms obscure trends 2007 Stéphane Gauvin FSA - ULaval

  9. French presidental 2007 Stéphane Gauvin FSA - ULaval

  10. Royal / Sarkozy 2007 Stéphane Gauvin FSA - ULaval

  11. Industry data 2007 Stéphane Gauvin FSA - ULaval

  12. Models • Parametric trend models • Robust estimator (M-reg) 2007 Stéphane Gauvin FSA - ULaval

  13. SSA • Singular Spectrum Analysis (SSA) (Golyandina et al. 2000) • Non parametric applications to the digital sphere • Bagchi & Mukhopadhyay (2006) (overall growth of the Internet) • Papagiannaki et al. (2005) (overall backbone traffic) • SSA applications • Ghil et al. (2002) (climatology) • Balazs & Chaloupka (2004) (biology) • Koelle & Pascual (2004) (epidemiology) • Antoniou et al. (2003) (wavelet model / Internet traffic) • Edwards (2006) (dissertation / US Navy related series) 2007 Stéphane Gauvin FSA - ULaval

  14. Caterpillar-SSA It is based on the idea of time series embedding into finite-dimensional space and following application of singular value decomposition (SVD) to the trajectory matrix (that is the result of time series embedding). The components of SVD are uniquely juxtaposed to the additive components of the original time series. Thereby we obtain the decomposition of the time series into additive components together with the information about them. This information is represented by the collection of singular vectors and signular values of the SVD. 2007 Stéphane Gauvin FSA - ULaval

  15. Caterpillar-SSA • Opérationnellement • Construire une matrice de vecteurs décalés (dim L/2) • Extraire les valeurs propres • Regrouper les eigen-vecteurs en trois groupes • Tendance (auto-corrélations varient lentement) • Cycles (auto-corrélations varient rapidement) • Bruit (cycles de fréquence arbitraire) 2007 Stéphane Gauvin FSA - ULaval

  16. Caterpillar-SSA 2007 Stéphane Gauvin FSA - ULaval

  17. Results - presidential 2007 Stéphane Gauvin FSA - ULaval

  18. Results - presidental 2007 Stéphane Gauvin FSA - ULaval

  19. Results - Industry 2007 Stéphane Gauvin FSA - ULaval

  20. Results - Industry 2007 Stéphane Gauvin FSA - ULaval

  21. Results - Industry 2007 Stéphane Gauvin FSA - ULaval

  22. Conclusions • Good signal-to-noise ratio • Estimation must be robust • SSA • Trend is easily extracted and follows closely the original series • Not robust to extreme values • M-NL • Dominant technique for large scale scenario • Sometimes, sensitive to seed values 2007 Stéphane Gauvin FSA - ULaval

  23. Next • Build a tracking system • M-NL to signal shifts • autoSSA to produce rich trend summaries • Explore forecasting models • Fitting and forecasting are not the same • Longer series to test rolling holdout samples • Validity issues • Anecdotal evidence of close tracking • Presidential series raises questions as to what the signal means 2007 Stéphane Gauvin FSA - ULaval

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