TY  - CPAPER 
AB  - 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.
AU  - Yoo, Do-il
DA  - 2016-05-26T04:50:03Z
DA  - 2016-05-26T04:50:03Z
DO  - 10.22004/ag.econ.236211
DO  - doi
ID  - 236211
KW  - Demand and Price Analysis
KW  - Research Methods/ Statistical Methods
KW  - Asymptotic data
KW  - Bayesian Structural Time Series Model
KW  - Price prediction
L1  - https://ageconsearch.umn.edu/record/236211/files/_yoo__AAEA__2016_Vegetable_Price_Prediction_Atypical_Web_Search_Data.pdf
L2  - https://ageconsearch.umn.edu/record/236211/files/_yoo__AAEA__2016_Vegetable_Price_Prediction_Atypical_Web_Search_Data.pdf
L4  - https://ageconsearch.umn.edu/record/236211/files/_yoo__AAEA__2016_Vegetable_Price_Prediction_Atypical_Web_Search_Data.pdf
LA  - eng
LA  - English
LK  - https://ageconsearch.umn.edu/record/236211/files/_yoo__AAEA__2016_Vegetable_Price_Prediction_Atypical_Web_Search_Data.pdf
N2  - 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.
PY  - 2016-05-26T04:50:03Z
PY  - 2016-05-26T04:50:03Z
T1  - Vegetable Price Prediction Using Atypical Web-Search Data
TI  - Vegetable Price Prediction Using Atypical Web-Search Data
UR  - https://ageconsearch.umn.edu/record/236211/files/_yoo__AAEA__2016_Vegetable_Price_Prediction_Atypical_Web_Search_Data.pdf
Y1  - 2016-05-26T04:50:03Z
T2  - 9768
ER  -