ecig 2007 modeling www time series
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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 - PowerPoint PPT PresentationTRANSCRIPT
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2007Stéphane Gauvin
FSA - ULaval
ECIG 2007
Modeling www time series
The research opportunity
A word on time series models
Data Models Results
What have we learned? Next steps
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2007Stéphane Gauvin
FSA - ULaval
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?
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2007Stéphane Gauvin
FSA - ULaval
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
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2007Stéphane Gauvin
FSA - ULaval
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
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2007Stéphane Gauvin
FSA - ULaval
Swammer
Build an intelligent set of queries to compute index
Shown to be close to survey data
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2007Stéphane Gauvin
FSA - ULaval
Illustrative data
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2007Stéphane Gauvin
FSA - ULaval
Robust or else
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2007Stéphane Gauvin
FSA - ULaval
Storms obscure trends
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2007Stéphane Gauvin
FSA - ULaval
French presidental
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2007Stéphane Gauvin
FSA - ULaval
Royal / Sarkozy
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2007Stéphane Gauvin
FSA - ULaval
Industry data
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2007Stéphane Gauvin
FSA - ULaval
Models
Parametric trend models
Robust estimator (M-reg)
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2007Stéphane Gauvin
FSA - ULaval
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)
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2007Stéphane Gauvin
FSA - ULaval
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.
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2007Stéphane Gauvin
FSA - ULaval
Caterpillar-SSA
Opérationnellement
1. Construire une matrice de vecteurs décalés (dim L/2)2. Extraire les valeurs propres3. Regrouper les eigen-vecteurs en trois groupes
1. Tendance (auto-corrélations varient lentement)2. Cycles (auto-corrélations varient rapidement)3. Bruit (cycles de fréquence arbitraire)
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2007Stéphane Gauvin
FSA - ULaval
Caterpillar-SSA
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2007Stéphane Gauvin
FSA - ULaval
Results - presidential
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2007Stéphane Gauvin
FSA - ULaval
Results - presidental
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2007Stéphane Gauvin
FSA - ULaval
Results - Industry
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2007Stéphane Gauvin
FSA - ULaval
Results - Industry
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2007Stéphane Gauvin
FSA - ULaval
Results - Industry
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2007Stéphane Gauvin
FSA - ULaval
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
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2007Stéphane Gauvin
FSA - ULaval
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