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Online big data-driven oil consumption forecasting with Google trends

Citations

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Cited by:

  1. Zhongchen Song & Tom Coupé, 2023. "Predicting Chinese consumption series with Baidu," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 21(3), pages 429-463, July.
  2. Stig Vinther Møller & Thomas Pedersen & Erik Christian Montes Schütte & Allan Timmermann, 2024. "Search and Predictability of Prices in the Housing Market," Management Science, INFORMS, vol. 70(1), pages 415-438, January.
  3. Jindai Zhang & Jinlou Zhao, 2022. "Trend- and Periodicity-Trait-Driven Gasoline Demand Forecasting," Energies, MDPI, vol. 15(10), pages 1-15, May.
  4. Dean Fantazzini & Julia Pushchelenko & Alexey Mironenkov & Alexey Kurbatskii, 2021. "Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg," Forecasting, MDPI, vol. 3(4), pages 1-30, October.
  5. Lean Yu & Yueming Ma, 2021. "A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting," Energies, MDPI, vol. 14(15), pages 1-26, July.
  6. van der Wielen, Wouter & Barrios, Salvador, 2021. "Economic sentiment during the COVID pandemic: Evidence from search behaviour in the EU," Journal of Economics and Business, Elsevier, vol. 115(C).
  7. Li, Mingchen & Cheng, Zishu & Lin, Wencan & Wei, Yunjie & Wang, Shouyang, 2023. "What can be learned from the historical trend of crude oil prices? An ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 123(C).
  8. Jin Shao & Lean Yu & Jingke Hong & Xianzhu Wang, 2025. "Forecasting house price index with social media sentiment: A decomposition–ensemble approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 216-241, January.
  9. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
  10. Li, Jingjing & Tang, Ling & Wang, Shouyang, 2020. "Forecasting crude oil price with multilingual search engine data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
  11. Michele Costola & Matteo Iacopini & Carlo R. M. A. Santagiustina, 2020. "Public Concern and the Financial Markets during the COVID-19 outbreak," Papers 2005.06796, arXiv.org.
  12. Wu, Binrong & Wang, Lin & Wang, Sirui & Zeng, Yu-Rong, 2021. "Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic," Energy, Elsevier, vol. 226(C).
  13. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2022. "The impact and role of COVID-19 uncertainty: A global industry analysis," International Review of Financial Analysis, Elsevier, vol. 80(C).
  14. Zhang, Yue-Jun & Li, Zhao-Chen, 2021. "Forecasting the stock returns of Chinese oil companies: Can investor attention help?," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 531-555.
  15. Daniel Borup & Erik Christian Montes Schütte, 2022. "In Search of a Job: Forecasting Employment Growth Using Google Trends," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 186-200, January.
  16. Yulin Yang & Jian Xu & Lean Yu, 2022. "Analysis of the Determinants and Mechanism of Mutton Price in Xinjiang Region of China," Sustainability, MDPI, vol. 14(20), pages 1-14, October.
  17. Tian, Yu-Xin & Zhang, Chuan, 2023. "An end-to-end deep learning model for solving data-driven newsvendor problem with accessibility to textual review data," International Journal of Production Economics, Elsevier, vol. 265(C).
  18. Escribano, Alvaro & Peña, Daniel & Ruiz, Esther, 2021. "30 years of cointegration and dynamic factor models forecasting and its future with big data: Editorial," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1333-1337.
  19. Osuji E. & Evans O., 2020. "Tourism Effects of Pandemics: New Insights from Novel Coronavirus," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 70(3-4), pages 56-65, July-Dece.
  20. Daekook Kang, 2021. "Box-office forecasting in Korea using search trend data: a modified generalized Bass diffusion model," Electronic Commerce Research, Springer, vol. 21(1), pages 41-72, March.
  21. Hardy, Nicolás & Ferreira, Tiago & Quinteros, Maria J. & Magner, Nicolás S., 2023. "“Watch your tone!”: Forecasting mining industry commodity prices with financial report tone," Resources Policy, Elsevier, vol. 86(PA).
  22. Jying-Nan Wang & Jiangze Du & Chonghui Jiang & Kin-Keung Lai, 2019. "Chinese Currency Exchange Rates Forecasting with EMD-Based Neural Network," Complexity, Hindawi, vol. 2019, pages 1-15, October.
  23. Niesert, Robin F. & Oorschot, Jochem A. & Veldhuisen, Christian P. & Brons, Kester & Lange, Rutger-Jan, 2020. "Can Google search data help predict macroeconomic series?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1163-1172.
  24. Liwen Ling & Dabin Zhang & Shanying Chen & Amin W. Mugera, 2020. "Can online search data improve the forecast accuracy of pork price in China?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 671-686, July.
  25. Berndt Jesenko & Christian Schlögl, 2021. "The effect of web of science subject categories on clustering: the case of data-driven methods in business and economic sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6785-6801, August.
  26. Costa, Alexandre Bonnet R. & Ferreira, Pedro Cavalcanti G. & Gaglianone, Wagner P. & Guillén, Osmani Teixeira C. & Issler, João Victor & Lin, Yihao, 2021. "Machine learning and oil price point and density forecasting," Energy Economics, Elsevier, vol. 102(C).
  27. Hu, Huanling & Wang, Lin & Peng, Lu & Zeng, Yu-Rong, 2020. "Effective energy consumption forecasting using enhanced bagged echo state network," Energy, Elsevier, vol. 193(C).
  28. Simonetto, Marco & Sgarbossa, Fabio & Battini, Daria & Govindan, Kannan, 2022. "Closed loop supply chains 4.0: From risks to benefits through advanced technologies. A literature review and research agenda," International Journal of Production Economics, Elsevier, vol. 253(C).
  29. Matheus Pereira Libório & Petr Iakovlevitch Ekel & Carlos Augusto Paiva Martins, 2023. "Economic analysis through alternative data and big data techniques: what do they tell about Brazil?," SN Business & Economics, Springer, vol. 3(1), pages 1-16, January.
  30. Mengyu Wang & Shay B. Cohen & Tiejun Ma, 2024. "Modeling News Interactions and Influence for Financial Market Prediction," Papers 2410.10614, arXiv.org.
  31. Chuan Zhang & Ao‐Yun Hu & Yu‐Xin Tian, 2023. "Daily tourism forecasting through a novel method based on principal component analysis, grey wolf optimizer, and extreme learning machine," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2121-2138, December.
  32. Chuan Zhang & Yu-Xin Tian & Ling-Wei Fan, 2020. "Improving the Bass model’s predictive power through online reviews, search traffic and macroeconomic data," Annals of Operations Research, Springer, vol. 295(2), pages 881-922, December.
  33. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
  34. Ovielt Baltodano Lopez & Federico Bassetti & Giulia Carallo & Roberto Casarin, 2022. "First-order integer-valued autoregressive processes with Generalized Katz innovations," Papers 2202.02029, arXiv.org, revised Dec 2024.
  35. Xie, Gang & Jiang, Fuxin & Zhang, Chengyuan, 2023. "A secondary decomposition-ensemble methodology for forecasting natural gas prices using multisource data," Resources Policy, Elsevier, vol. 85(PA).
  36. Cerqueti, Roy & Ficcadenti, Valerio & Mattera, Raffaele, 2024. "Investors’ attention and network spillover for commodity market forecasting," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
  37. Aaronson, Daniel & Brave, Scott A. & Butters, R. Andrew & Fogarty, Michael & Sacks, Daniel W. & Seo, Boyoung, 2022. "Forecasting unemployment insurance claims in realtime with Google Trends," International Journal of Forecasting, Elsevier, vol. 38(2), pages 567-581.
  38. Cheng, Zishu & Li, Mingchen & Sun, Yuying & Hong, Yongmiao & Wang, Shouyang, 2024. "Climate change and crude oil prices: An interval forecast model with interval-valued textual data," Energy Economics, Elsevier, vol. 134(C).
  39. Andreea Avramescu & Arkadiusz Wiśniowski, 2021. "Now-casting Romanian migration into the United Kingdom by using Google Search engine data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 45(40), pages 1219-1254.
  40. Rodrigo Mulero & Alfredo García-Hiernaux, 2021. "Forecasting Spanish unemployment with Google Trends and dimension reduction techniques," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(3), pages 329-349, September.
  41. Li, Jieyi & Qian, Shuangyue & Li, Ling & Guo, Yuanxuan & Wu, Jun & Tang, Ling, 2024. "A novel secondary decomposition method for forecasting crude oil price with twitter sentiment," Energy, Elsevier, vol. 290(C).
  42. Lin, Ling & Jiang, Yong & Xiao, Helu & Zhou, Zhongbao, 2020. "Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(C).
  43. Zhang, Dingxuan & Sun, Yuying & Duan, Hongbo & Hong, Yongmiao & Wang, Shouyang, 2023. "Speculation or currency? Multi-scale analysis of cryptocurrencies—The case of Bitcoin," International Review of Financial Analysis, Elsevier, vol. 88(C).
  44. Chen, Rongda & Wei, Bo & Jin, Chenglu & Liu, Jia, 2021. "Returns and volatilities of energy futures markets: Roles of speculative and hedging sentiments," International Review of Financial Analysis, Elsevier, vol. 76(C).
  45. David Coble & Pablo Pincheira, 2021. "Forecasting building permits with Google Trends," Empirical Economics, Springer, vol. 61(6), pages 3315-3345, December.
  46. Emmanuel Sirimal Silva & Hossein Hassani & Dag Øivind Madsen & Liz Gee, 2019. "Googling Fashion: Forecasting Fashion Consumer Behaviour Using Google Trends," Social Sciences, MDPI, vol. 8(4), pages 1-23, April.
  47. Zhang, Chuan & Tian, Yu-Xin & Fan, Zhi-Ping, 2022. "Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1005-1024.
  48. Liang, Chao & Wang, Lu & Duong, Duy, 2024. "More attention and better volatility forecast accuracy: How does war attention affect stock volatility predictability?," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 1-19.
  49. Lu, Quanying & Li, Yuze & Chai, Jian & Wang, Shouyang, 2020. "Crude oil price analysis and forecasting: A perspective of “new triangle”," Energy Economics, Elsevier, vol. 87(C).
  50. Saida Filali & Nassima Faraj, 2022. "Marketing of SMEs in the digital age: Challenges and issues," Post-Print hal-03895605, HAL.
  51. Gao, Feng & Chi, Hong & Shao, Xueyan, 2021. "Forecasting residential electricity consumption using a hybrid machine learning model with online search data," Applied Energy, Elsevier, vol. 300(C).
  52. Qin, Quande & Xie, Kangqiang & He, Huangda & Li, Li & Chu, Xianghua & Wei, Yi-Ming & Wu, Teresa, 2019. "An effective and robust decomposition-ensemble energy price forecasting paradigm with local linear prediction," Energy Economics, Elsevier, vol. 83(C), pages 402-414.
  53. Mohammad Reza Farzanegan & Mehdi Feizi & Saeed Malek Sadati, 2020. "Google It Up! A Google Trends-based analysis of COVID-19 outbreak in Iran," MAGKS Papers on Economics 202017, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  54. Costola, Michele & Iacopini, Matteo & Santagiustina, Carlo R.M.A., 2021. "Google search volumes and the financial markets during the COVID-19 outbreak," Finance Research Letters, Elsevier, vol. 42(C).
  55. Zhang, Xiaokong & Chai, Jian & Tian, Lingyue & Yang, Ying & Zhang, Zhe George & Pan, Yue, 2023. "Forecast and structural characteristics of China's oil product consumption embedded in bottom-line thinking," Energy, Elsevier, vol. 278(PA).
  56. Tat-Huei Cham & Jun-Hwa Cheah & Mumtaz Ali Memon & Kim-Shyan Fam & Józsa László, 2022. "Digitalization and its impact on contemporary marketing strategies and practices," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(2), pages 103-105, June.
  57. Prasanth, Sikakollu & Singh, Uttam & Kumar, Arun & Tikkiwal, Vinay Anand & Chong, Peter H.J., 2021. "Forecasting spread of COVID-19 using google trends: A hybrid GWO-deep learning approach," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
  58. Lolić, Ivana & Matošec, Marina & Sorić, Petar, 2024. "DIY google trends indicators in social sciences: A methodological note," Technology in Society, Elsevier, vol. 77(C).
  59. Gang Xie & Xin Li & Yatong Qian & Shouyang Wang, 2021. "Forecasting tourism demand with KPCA-based web search indexes," Tourism Economics, , vol. 27(4), pages 721-743, June.
  60. Zhao, Lu-Tao & Zheng, Zhi-Yi & Wei, Yi-Ming, 2023. "Forecasting oil inventory changes with Google trends: A hybrid wavelet decomposer and ARDL-SVR ensemble model," Energy Economics, Elsevier, vol. 120(C).
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