Knowing the prices of agricultural commodities in advance can provide governments, farmers, and consumers with various advantages, including a clearer understanding of the market, planning business strategies, and adjusting personal finances. Thus, there have been many efforts to predict the future prices of agricultural commodities in the past. For example, researchers have attempted to predict prices by extracting price quotes, using sentiment analysis algorithms, through statistical information from news stories, and by other means. In this paper, we propose a methodology that predicts the daily retail price of pork in the South Korean domestic market based on news articles by incorporating deep learning and topic modeling techniques. To do this, we utilized news articles and retail price data from 2010 to 2019. We initially applied a topic modeling technique to obtain relevant keywords that can express price fluctuations. Based on these keywords, we constructed prediction models using statistical, machine learning, and deep learning methods. The experimental results show that there is a strong relationship between the meaning of news articles and the price of pork.

Big data have become a core technology to provide innovative solutions in numerical applications and services in many fields

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.

In the manufacturing industry, the process capability index (Cpk) measures the level and capability required to improve the processes. However, the Cpk is not enough to represent the process capability and performance of the manufacturing processes. In other words, considering that the smart manufacturing environment can accommodate the big data collected from various facilities, we need to understand the state of the process by comprehensively considering diverse factors contained in the manufacturing. In this paper, a two-stage method is proposed to analyze the process quality performance (PQP) and predict future process quality. First, we propose the PQP as a new measure for representing process capability and performance, which is defined by a composite statistical process analysis of such factors as manufacturing cycle time analysis, process trajectory of abnormal detection, statistical process control analysis, and process capability control analysis. Second, PQP analysis results are used to predict and estimate the stability of the production process using a long short-term memory (LSTM) neural network, which is a deep learning algorithm-based method. The present work compares the LSTM prediction model with the random forest, autoregressive integrated moving average, and artificial neural network models to convincingly demonstrate the effectiveness of our proposed approach. Notably, the LSTM model achieved higher accuracy than the other models.

한국멀티미디어학회(The Journal of Curriculum Evaluation)