Vegetable Price Prediction Using Unstructured Web-Based Data: An Application to Garlic, Onion, and Pepper in Korea

• 발행기관: 한국농업경제학회
• 저자명: Do-il Yoo
• 간행물 정보: 『농업경제연구』57권 3호, 209~233쪽, 전체 25쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2016.09.30

영문 초록

Focusing on the recent Big-Data boom, we develop vegetable price prediction models incorporating unstructured web-based data obtained from various online web-sites such as news, blogs, cafes, and so on. For empirical analysis, we employ Bayesian structural time series (BSTS) models with four unstructured indices using a text-mining tool, Textom; the amount of buzzwords, the amount of search keywords, the term frequency-inverse document frequency (TF-IDF), and the degree-centrality-weighted term frequency (DCTF). Then, the models are applied to three vegetable products of garlic, onion, and pepper in Korea. Results show that prediction performances can be remarkably improved by the introduction of unstructured indices for all products. The degree of improvement and the selection of unstructured indices can vary by vegetable products with their market and web-based environments.

목차

I. Introduction

II. Model

III. Data

IV. Results

V. Conclusion

키워드

unstructured web-based data Bayesian structural time series price prediction