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Forecasting oil consumption with attention-based IndRNN optimized by adaptive differential evolution.

Binrong Wu1, Lin Wang1, Sheng-Xiang Lv2

  • 1School of Management, Huazhong University of Science and Technology, 430074 Wuhan, China.

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|July 5, 2022
PubMed
Summary

This study introduces a news-based method using convolutional neural networks (CNN) and an attention-based JADE-IndRNN model to predict monthly oil consumption, improving accuracy amidst COVID-19 volatility.

Keywords:
COVID-19 pandemicDeep learningIndependent recurrent neural networkOil consumption forecastingOnline newsText mining

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Area of Science:

  • Energy economics
  • Data science
  • Machine learning

Background:

  • Accurate oil consumption prediction is crucial for supply chain management.
  • The COVID-19 pandemic introduced significant volatility and uncertainty in oil consumption trends.
  • Online information, particularly news, offers potential for enhanced prediction accuracy.

Purpose of the Study:

  • To develop a novel news-based methodology for predicting oil consumption.
  • To evaluate the contribution of text features from online news to oil consumption prediction.
  • To propose an advanced forecasting model integrating news data.

Main Methods:

  • Utilized a convolutional neural network (CNN) to automatically extract information from online news.
  • Developed an attention-based JADE-IndRNN model combining adaptive differential evolution (JADE) and an attention-based independent recurrent neural network (IndRNN).
  • Integrated extracted text features into the forecasting model to predict monthly oil consumption.

Main Results:

  • The proposed news-based methodology significantly improved prediction accuracy compared to traditional methods lacking online news data.
  • Text features from online news provided valuable insights, especially concerning COVID-19 related policies.
  • The attention-based JADE-IndRNN model demonstrated effectiveness in forecasting volatile oil consumption patterns.

Conclusions:

  • News-based prediction models offer a significant advantage for forecasting oil consumption, particularly during uncertain periods like the COVID-19 pandemic.
  • The integration of CNN for news feature extraction and attention-based JADE-IndRNN for forecasting is a promising approach.
  • Online news content can capture critical contextual information influencing economic trends such as oil consumption.