English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

It is believed that the huge amount of information delivered to the consumers through mass media, including television and social networks, may affect consumers’ behavior. The purpose of this study was to forecast the amount required to purchase pork belly meat by using unstructured data such as broadcast news, TV programs/shows and social network as well as structured data such as consumer panel data, retail and wholesale prices and production outputs in order to prove that mass media data release can occur ahead of actual economic activities and consumer behavior can be predicted by using these data. By using structured and unstructured data from 2010 to 2016 and five forecasting algorithms (autoregressive exogenous model and vector error correction model for time series, gradient boosting and random forest for machine learning, and long short-term memory for recurrent neural network), the amounts required to purchase pork belly meat in 2017 were forecasted and compared with the actual amounts to validate model accuracy. Our findings suggest that when unstructured data were combined with structured data, the forecast pattern is improved. To date, our study is the first report that forecasts the demand of pork meat by using structured and unstructured data.

Forecasts of the Amount Purchase Pork Meat by Using Structured and Unstructured Big Data