2015/12/26 by Roberto Rivera, Rivera, Roberto
Business, Management and Accounting · Mathematics · Medicine · Social Sciences · #Applications (stat.AP) #Consumer Market Behavior and Pricing #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #stat.AP
paper · pdf · doi:10.48550/arxiv.1512.08097
openalex publication_date 2015/12/26 · arxiv created 2016/02/26 · arxiv updated 2016/02/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recently, studies have used search query volume (SQV) data to forecast a given process of interest. However, Google Trends SQV data comes from a periodic sample of queries. As a result, Google Trends data is different every week. We propose a Dynamic Linear Model that treats SQV data as a representation of an unobservable process. We apply our model to forecast the number of hotel nonresident registrations in Puerto Rico using SQV data downloaded in 11 different occasions. The model provides better inference on the association between the number of hotel nonresident registrations and SQV than using Google Trends data retrieved only on one occasion. Furthermore, our model results in more realistic prediction intervals of forecasts. However, compared to simpler models we only find evidence of better performance for our model when making forecasts on a horizon of over 6 months.