Nowcasting Business Performance

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talk by Francois Bouet at Data Science London, 28/11/12

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Nowcasting Business Performance

  1. 1. NOWCASTING BUSINESS PERFORMANCEmiércoles, 28 de noviembre de 12
  2. 2. GROWTH INTELLIGENCE What we do Classification of companies Revenue estimation What we use Machine Learning Times Series methodsmiércoles, 28 de noviembre de 12
  3. 3. SOME OF OUR CLIENTSmiércoles, 28 de noviembre de 12
  4. 4. NOWCASTING Estimating the value of a time series not readily available at present presentmiércoles, 28 de noviembre de 12
  5. 5. NOWCASTING presentmiércoles, 28 de noviembre de 12
  6. 6. NOWCASTING Previously called short-term forecasting forecasting More an approach and a goal than a different theory and fieldmiércoles, 28 de noviembre de 12
  7. 7. NOWCASTING USE CASES Weather nowcasting Search-based nowcasting GDP nowcastingmiércoles, 28 de noviembre de 12
  8. 8. WEATHER NOWCASTING Simplified model that is applied quickly Uses weather models Forecast at location x given weather at y → Not applicable to other fieldsmiércoles, 28 de noviembre de 12
  9. 9. SEARCH-BASED NOWCASTING Popularized by Google Recent successes Flu predictions Consumer behaviour travel, movies and products Based on Google’s data, simple AR models Only used to study what people are searching formiércoles, 28 de noviembre de 12
  10. 10. miércoles, 28 de noviembre de 12
  11. 11. miércoles, 28 de noviembre de 12
  12. 12. GDP NOWCASTING Field with the most generic research Major research since the 90s GDP released quarterly with further revisions 1000s of signals for GDP nowcasting Industrial production, unemployment, confidence surveys, retail sales, ...miércoles, 28 de noviembre de 12
  13. 13. GDP NOWCASTING Vector auto-regression and the “jagged edge” Present Different frequencies, different lag, missing datamiércoles, 28 de noviembre de 12
  14. 14. miércoles, 28 de noviembre de 12
  15. 15. miércoles, 28 de noviembre de 12
  16. 16. miércoles, 28 de noviembre de 12
  17. 17. Patents Search results Advertisement spending LinkedIn info Web traffic Assets Tweets Website Liabilities Press updatesmiércoles, 28 de noviembre de 12
  18. 18. TIE WITH “BIG DATA” Need to gather signals in large quantity Machine learning as a pre-processing step and to integrate discrete events Example: companies in a sector which receive investmentmiércoles, 28 de noviembre de 12
  19. 19. TIE WITH ESTIMATION THEORY Beneath all this: Getting to a variable not directly observable with the help of measured signals Replacing probability distribution from physical models with machine learned knowledgemiércoles, 28 de noviembre de 12
  20. 20. METHODOLOGIES Vector auto-regression Challenge with large number of signals (predictors): Curse of dimensionality when applying VAR Machine Learning approach Own solution: zigguratmiércoles, 28 de noviembre de 12
  21. 21. TIME SERIES + MACHINE LEARNING avg, std dev, model params Δrevenuemiércoles, 28 de noviembre de 12
  22. 22. OUR PIPELINE FOR NOWCASTING Clustering companies in sets (ML) Signals gathering Time Series processing ML with model for each cluster > Revenue for each company and each clustermiércoles, 28 de noviembre de 12
  23. 23. TECHNOLOGIESmiércoles, 28 de noviembre de 12
  24. 24. DATA SCIENCE AT GROWTH INTELLIGENCE :Dmiércoles, 28 de noviembre de 12

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