000236211 001__ 236211 000236211 005__ 20210819131439.0 000236211 0247_ $$2doi$$a10.22004/ag.econ.236211 000236211 037__ $$a333-2016-14792 000236211 041__ $$aeng 000236211 245__ $$aVegetable Price Prediction Using Atypical Web-Search Data 000236211 260__ $$c2016-05-26T04:50:03Z 000236211 269__ $$a2016-05-26T04:50:03Z 000236211 300__ $$a20 000236211 336__ $$aConference Paper/ Presentation 000236211 490__ $$a9768 000236211 520__ $$aOur study focuses on 3 vegetables mainly purchased in Korea; onion, garlic, and dried red pepper. We develop atypical index reflecting consumers’ attention on those vegetables from social network service (SNS) websites and major portal sites such as Google. Specifically, using text mining program, we gather associate web-search data, making simple query data measuring frequency on websites and Term Frequency – Inverse Document Frequency (TF-IDF) considering weights of core keywords on websites. We introduce those asymptotic indexes into the Bayesian structural time series models with climate factors impacting vegetable prices. Results show that the introduction of atypical web-search data can improve vegetable price prediction power compared to pure time-series models without atypical indexes. 000236211 546__ $$aEnglish 000236211 650__ $$aDemand and Price Analysis 000236211 650__ $$aResearch Methods/ Statistical Methods 000236211 6531_ $$aAsymptotic data 000236211 6531_ $$aBayesian Structural Time Series Model 000236211 6531_ $$aPrice prediction 000236211 700__ $$aYoo, Do-il 000236211 773__ $$d2016 000236211 8564_ $$9b34dccf5-540c-4500-8481-d6d532960b77$$s423893$$uhttps://ageconsearch.umn.edu/record/236211/files/_yoo__AAEA__2016_Vegetable_Price_Prediction_Atypical_Web_Search_Data.pdf 000236211 887__ $$ahttp://purl.umn.edu/236211 000236211 909CO $$ooai:ageconsearch.tind.io:236211$$pGLOBAL_SET 000236211 912__ $$nSubmitted by Do-il Yoo (scydl8@gmail.com) on 2016-05-26T04:50:03Z No. of bitstreams: 1 (yoo)(AAEA)(2016)Vegetable_Price_Prediction_Atypical_Web_Search_Data.pdf: 423893 bytes, checksum: 1b96370a379a5a35cbf8f73398327d36 (MD5) 000236211 912__ $$nMade available in DSpace on 2016-05-26T04:50:03Z (GMT). No. of bitstreams: 1 (yoo)(AAEA)(2016)Vegetable_Price_Prediction_Atypical_Web_Search_Data.pdf: 423893 bytes, checksum: 1b96370a379a5a35cbf8f73398327d36 (MD5) 000236211 980__ $$a333 000236211 982__ $$gAgricultural and Applied Economics Association>2016 Annual Meeting, July 31-August 2, Boston, Massachusetts