| Autor: |
شهرام فتاحي, سعيد کيان پور, کيومرث سهيلي |
| Zdroj: |
Journal of Mineral Resources Engineering / Muhandisī-i Manābi̒-i Ma̒danī; Summer2023, Vol. 8 Issue 2, p83-97, 15p |
| Abstrakt: |
This study uses data modeling and text mining techniques for oil price predictions. To improve the model's explanatory capability, text features from internet news articles on crude oil are automatically extracted using convolutional neural networks. Additionally, various time series models employ a state analysis approach called convolution. The years 2021 to 2011 saw the collection of almost 13000 news items, and it was discovered that text mining and data from large Internet-based apps perform better for prediction than other approaches. This means that it is pretty fair to say that there is a parallel link between news headlines, those headlines, and searches in the Google search engine. This relationship is highly appropriate for correctly forecasting the price of crude oil. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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