Forecasting Crude Oil Price Using Event Extraction
نویسندگان
چکیده
Research on crude oil price forecasting has attracted tremendous attention from scholars and policymakers due to its significant effect the global economy. Besides supply demand, prices are largely influenced by various factors, such as economic development, financial markets, conflicts, wars, political events. Most previous research treats a time series or econometric variable prediction problem. Although recently there have been researches considering effects of real-time news events, most these works mainly use raw headlines topic models extract text features without profoundly exploring event information. In this study, novel framework, AGESL, is proposed deal with our approach, an open domain extraction algorithm utilized underlying related sentiment analysis used massive news. Then deep neural network integrating features, sentimental historical built predict future prices. Empirical experiments performed West Texas Intermediate (WTI) data, results show that approach obtains superior performance compared several benchmark methods.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2021
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2021.3124802