@article{Yoo:236211,
      recid = {236211},
      author = {Yoo, Do-il},
      title = {Vegetable Price Prediction Using Atypical Web-Search Data},
      address = {2016-05-26T04:50:03Z},
      number = {333-2016-14792},
      series = {9768},
      pages = {20},
      month = {May},
      year = {2016},
      abstract = {Our study focuses on 3 vegetables mainly purchased in  Korea; onion, garlic, and dried red pepper. We develop  atypical index reflecting consumers’ attention on those  vegetables from social network service (SNS) websites and  major portal sites such as Google. Specifically, using text  mining program, we gather associate web-search data, making  simple query data measuring frequency on websites and Term  Frequency – Inverse Document Frequency (TF-IDF) considering  weights of core keywords on websites. We introduce those  asymptotic indexes into the Bayesian structural time series  models with climate factors impacting vegetable prices.  Results show that the introduction of atypical web-search  data can improve vegetable price prediction power compared  to pure time-series models without atypical indexes.},
      url = {http://ageconsearch.umn.edu/record/236211},
      doi = {https://doi.org/10.22004/ag.econ.236211},
}