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Online big data-driven oil consumption forecasting with Google trends

  • Published:2019-01 Issue:1 Volume:35 Page:213-223
  • ISSN:0169-2070
  • Container-title:International Journal of Forecasting
  • language:en
  • Short-container-title:International Journal of Forecasting

Author:

Yu Lean,Zhao Yaqing,Tang Ling,Yang Zebin

Funder

National Natural Science Foundation of China

National Program for Support of Top Notch Young Professionals

National Program on Key Research Project of China

Beijing Advanced Innovation Centre for Soft Matter Science and Engineering

Publisher

Elsevier BV

Subject

Business and International Management

Reference57 articles.

1. Improved supply chain management based on hybrid demand forecasts;Aburto;Applied Soft Computing,2007

2. ARIMA forecasting of primary energy production and consumption in Turkey: 1923-2006;Albayrak;Enerji, Piyasa ve Düzenleme,2010

3. Can internet searches forecast tourism inflows?;Artola;International Journal of Manpower,2015

4. Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand estimation of oil in Iran;Assareh;Energy,2010

5. Does disagreement among oil price forecasters reflect volatility? Evidence from the ECB Surveys;Atalla;International Journal of Forecasting,2016

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