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Predicting the Present

2. 2. Problem statement. Government agencies and other organizations produce monthly reports on economic activity Retail Sales House Sales Automotive Sales UnemploymentProblems with reportsCompilation delay of several weeksSubsequent revisions Sample size may be smallNot available at all ge

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Predicting the Present

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    1. 1 Predicting the Present With Google Trends

    2. 2

    3. Clearings Sectors We have 30 top categories and under the 30 categories, we have 274 subcategories and we have 4 layers of categories with 753 verticals. Context This market map shows the top 2 layers of categories area represents the relative query volume and the color represent the relative growth rate over the year. From this map, we see that Entertainment categories have high search volume and its been growing more than other sectors such as real estate and travel. For all 753 verticals, we could see the search volume changes over time and depending on the sector of your interest, this data could give some insight on the customer behavior. Transition Lets look at the example of Real estate. As we all know, real estate sector hasnt been doing that well this year, and you can see it from the color of that sector. Clearings Sectors We have 30 top categories and under the 30 categories, we have 274 subcategories and we have 4 layers of categories with 753 verticals. Context This market map shows the top 2 layers of categories area represents the relative query volume and the color represent the relative growth rate over the year. From this map, we see that Entertainment categories have high search volume and its been growing more than other sectors such as real estate and travel. For all 753 verticals, we could see the search volume changes over time and depending on the sector of your interest, this data could give some insight on the customer behavior. Transition Lets look at the example of Real estate. As we all know, real estate sector hasnt been doing that well this year, and you can see it from the color of that sector.

    4. Real Estate

    5. Clearings Context Transition Clearings Context Transition

    6. 6

    7. 7

    8. 8

    9. 9 Depicting trends

    10. 10

    11. 11

    12. Model

    13. 13

    14. 14 Get new pics Plot --- Mortgage rate vs. Home financing Get new pics Plot --- Mortgage rate vs. Home financing

    15. 15

    16. 16

    17. 17 Travel

    18. 18

    19. 19

    20. 20

    21. 21

    22. 22

    23. 23

    24. 24 Automobiles

    25. 25

    26. 26

    27. 27

    28. 28

    29. 29

    30. 30

    31. 31 Unemployment

    33. Initial claims is an important leading indicator

    34. Google Trends data [Search Insights screenshot]

    35. Initial Claims and Google Trends

    36. Strong Autocorrelation in Initial Claims

    37. Initial Claims Before/After Recession Started

    38. Time Window for Analysis

    39. Model

    40. Long Term Model: Prediction Comparison with MAE With Google Trends, the out-of-sample prediction MAE decreases by 16.84%. Prediction with rolling window from 1/11/2009 to 4/12/2009 Prediction Error at t: Mean Absolute Error:

    41. Short Term Model: Prediction Comparison with MAE With Google Trends, the out-of-sample prediction MAE decreases by 19.23%. Prediction errors are within the same range as LT Model. Fit improvement is better with ST Model.

    42. Summary Google Trends significantly improves out-of-sample prediction of state unemployment, up to 18 days in advance of data release. Mean absolute error for out-of-sample predictions declines by 16.84% for LT Model and 19.23% for ST Model. Further work Can examine metro level data Other local data (real estate)? Combine with other predictors Detect turning points?

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