Volatility Forecasting for Crude Oil based on Text Information and Deep Learning PSO‐LSTM Model

2021 ◽  
Author(s):  
Xingrui Jiao ◽  
Yuping Song ◽  
Yang Kong ◽  
Xiaolong Tang
Keyword(s):  
Deep Learning ◽  
Crude Oil ◽  
Volatility Forecasting ◽  
Text Information

The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China

2021 ◽  
Vol 73 ◽  
pp. 102173
Author(s):  
Zibo Niu ◽  
Yuanyuan Liu ◽  
Wang Gao ◽  
Hongwei Zhang
Keyword(s):  
Crude Oil ◽  
Futures Markets ◽  
Volatility Forecasting ◽  
Oil Futures ◽  
Crude Oil Futures

Volatility Forecasting of the Crude Oil Market

2015 ◽  
pp. 199-214
Author(s):  
Desheng Dash Wu ◽  
David L. Olson
Keyword(s):  
Crude Oil ◽  
Volatility Forecasting ◽  
Oil Market

A deep learning ensemble approach for crude oil price forecasting

2017 ◽  
Vol 66 ◽  
pp. 9-16 ◽  
Author(s):  
Yang Zhao ◽  
Jianping Li ◽  
Lean Yu
Keyword(s):  
Deep Learning ◽  
Crude Oil ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Ensemble Approach

Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets

2019 ◽  
Vol 81 ◽  
pp. 52-62 ◽  
Author(s):  
Yixiang Chen ◽  
Feng Ma ◽  
Yaojie Zhang
Keyword(s):  
Crude Oil ◽  
Stock Markets ◽  
Volatility Forecasting ◽  
New Evidence ◽  
The U.S

scholarly journals DLI-IT: A Deep Learning Approach to Drug Label Identification Through Image and Text Embedding

2020 ◽  
Author(s):  
Xiangwen Liu ◽  
Joe Meehan ◽  
Weida Tong ◽  
Leihong Wu ◽  
Xiaowei Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Package Insert ◽  
Drug Distribution ◽  
Labor Cost ◽  
Drug Label ◽  
Accurate Identification ◽  
Learning Framework ◽  
Universal Sentence ◽  
Reference Images ◽  
Text Information

Abstract [Background] Drug label, or packaging insert play a significant role in all the operations from production through drug distribution channels to the end consumer. Image of the label also called Display Panel or label could be used to identify illegal, illicit, unapproved and potentially dangerous drugs. Due to the time-consuming process and high labor cost of investigation, an artificial intelligence-based deep learning model is necessary for fast and accurate identification of the drugs. [Methods] In addition to image-based identification technology, we take advantages of rich text information on the pharmaceutical package insert of drug label images. In this study, we developed the Drug Label Identification through Image and Text embedding model (DLI-IT) to model text-based patterns of historical data for detection of suspicious drugs. In DLI-IT, we first trained a Connectionist Text Proposal Network (CTPN) to crop the raw image into sub-images based on the text. The texts from the cropped sub-images are recognized independently through the Tesseract OCR Engine and combined as one document for each raw image. Finally, we applied universal sentence embedding to transform these documents into vectors and find the most similar reference images to the test image through the cosine similarity. [Results] We trained the DLI-IT model on 1749 opioid and 2365 non-opioid drug label images. The model was then tested on 300 external opioid drug label images, the result demonstrated our model achieves up-to 88% of the precision in drug label identification, which outperforms previous image-based or text-based identification method by up-to 35% improvement. [Conclusion] To conclude, by combining Image and Text embedding analysis under deep learning framework, our DLI-IT approach achieved a competitive performance in advancing drug label identification.

scholarly journals Further Study of the DEA-Based Framework for Performance Evaluation of Competing Crude Oil Prices’ Volatility Forecasting Models

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 827 ◽  
Author(s):  
Zhongbao Zhou ◽  
Qianying Jin ◽  
Jian Peng ◽  
Helu Xiao ◽  
Shijian Wu
Keyword(s):  
Crude Oil ◽  
Oil Prices ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Volatility Forecasting ◽  
Analysis Model ◽  
Super Efficiency ◽  
Forecasting Models ◽  
Crude Oil Prices ◽  
Efficiency Data

The super-efficiency data envelopment analysis model is innovative in evaluating the performance of crude oil prices’ volatility forecasting models. This multidimensional ranking, which takes account of multiple criteria, gives rise to a unified decision as to which model performs best. However, the rankings are unreliable because some efficiency scores are infeasible solutions in nature. What’s more, the desirability of indexes is worth discussing so as to avoid incorrect rankings. Hence, herein we introduce four models, which address the issue of undesirable characteristics of indexes and infeasibility of the super efficiency models. The empirical results reveal that the new rankings are more robust and quite different from the existing results.

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