nep-for New Economics Papers
on Forecasting
Issue of 2021‒12‒13
two papers chosen by
Rob J Hyndman
Monash University

  1. Forecasting Regional Milk Production Quantity: A Comparison of Regression Models and Machine Learning By Baaken, Dominik; Hess, Sebastian
  2. Forecasting Crude Oil Price Using Event Extraction By Jiangwei Liu; Xiaohong Huang

  1. By: Baaken, Dominik; Hess, Sebastian
    Keywords: Livestock Production/Industries
    Date: 2021–08
  2. By: Jiangwei Liu; Xiaohong Huang
    Abstract: Research on crude oil price forecasting has attracted tremendous attention from scholars and policymakers due to its significant effect on the global economy. Besides supply and demand, crude oil prices are largely influenced by various factors, such as economic development, financial markets, conflicts, wars, and political events. Most previous research treats crude oil price forecasting as a time series or econometric variable prediction problem. Although recently there have been researches considering the effects of real-time news events, most of these works mainly use raw news headlines or topic models to extract text features without profoundly exploring the event information. In this study, a novel crude oil price forecasting framework, AGESL, is proposed to deal with this problem. In our approach, an open domain event extraction algorithm is utilized to extract underlying related events, and a text sentiment analysis algorithm is used to extract sentiment from massive news. Then a deep neural network integrating the news event features, sentimental features, and historical price features is built to predict future crude oil prices. Empirical experiments are performed on West Texas Intermediate (WTI) crude oil price data, and the results show that our approach obtains superior performance compared with several benchmark methods.
    Date: 2021–11

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