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Comprehensive commodity price forecasting framework using text mining methods

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  • Wuyue An
  • Lin Wang
  • Dongfeng Zhang

Abstract

Exploiting advanced and appropriate methods to construct high‐quality features from different types of data becomes crucial in agricultural futures price forecasting. Thus, this study develops a comprehensive commodity price forecasting framework using text mining methods. First, the modal features of the price series are extracted using the proposed Integrated‐EEMD‐VMD‐SE method, and dynamic topic sentiment features are constructed from Weibo texts using the proposed dynamic topic model joint sentiment analysis method. Second, combined with statistical variables, lag order selection and feature selection are performed on these comprehensive factors. Finally, 12 comparative prediction models are designed based on random forest (RF), long short‐term memory (LSTM), and multilayer perceptron (MLP), and empirical analysis is carried out on two cases of pork prices and soybean futures prices. The experimental results show that the proposed prediction framework has high prediction accuracy, and the mean absolute percentage error (MAPE) values are 1.00 and 0.92, respectively. The constructed time series modal features and dynamic topic sentiment features can significantly improve the performance of the prediction model.

Suggested Citation

  • Wuyue An & Lin Wang & Dongfeng Zhang, 2023. "Comprehensive commodity price forecasting framework using text mining methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1865-1888, November.
    • Handle: RePEc:wly:jforec:v:42:y:2023:i:7:p:1865-1888
    DOI: 10.1002/for.2985
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    1. Lv, Sheng-Xiang & Wang, Lin, 2023. "Multivariate wind speed forecasting based on multi-objective feature selection approach and hybrid deep learning model," Energy, Elsevier, vol. 263(PE).
    2. 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).
    3. Xingrui Jiao & Yuping Song & Yang Kong & Xiaolong Tang, 2022. "Volatility forecasting for crude oil based on text information and deep learning PSO‐LSTM model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 933-944, August.
    4. Wang, Jue & Wang, Zhen & Li, Xiang & Zhou, Hao, 2022. "Artificial bee colony-based combination approach to forecasting agricultural commodity prices," International Journal of Forecasting, Elsevier, vol. 38(1), pages 21-34.
    5. Li, Jianping & Li, Guowen & Liu, Mingxi & Zhu, Xiaoqian & Wei, Lu, 2022. "A novel text-based framework for forecasting agricultural futures using massive online news headlines," International Journal of Forecasting, Elsevier, vol. 38(1), pages 35-50.
    6. Qiang Gao & Xiao Huang & Ke Dong & Zhentao Liang & Jiang Wu, 2022. "Semantic-enhanced topic evolution analysis: a combination of the dynamic topic model and word2vec," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1543-1563, March.
    7. Ga-Ae Ryu & Aziz Nasridinov & HyungChul Rah & Kwan-Hee Yoo, 2020. "Forecasts of the Amount Purchase Pork Meat by Using Structured and Unstructured Big Data," Agriculture, MDPI, vol. 10(1), pages 1-14, January.
    8. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    9. Massaoudi, Mohamed & Refaat, Shady S. & Chihi, Ines & Trabelsi, Mohamed & Oueslati, Fakhreddine S. & Abu-Rub, Haitham, 2021. "A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting," Energy, Elsevier, vol. 214(C).
    10. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    11. 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.
    12. Sun, Na & Zhou, Jianzhong & Chen, Lu & Jia, Benjun & Tayyab, Muhammad & Peng, Tian, 2018. "An adaptive dynamic short-term wind speed forecasting model using secondary decomposition and an improved regularized extreme learning machine," Energy, Elsevier, vol. 165(PB), pages 939-957.
    13. Peng, Lu & Wang, Lin & Xia, De & Gao, Qinglu, 2022. "Effective energy consumption forecasting using empirical wavelet transform and long short-term memory," Energy, Elsevier, vol. 238(PB).
    14. Bai, Yun & Li, Xixi & Yu, Hao & Jia, Suling, 2022. "Crude oil price forecasting incorporating news text," International Journal of Forecasting, Elsevier, vol. 38(1), pages 367-383.
    15. Jingfei Zhang & Zhicheng Zheng & Lijun Zhang & Yaochen Qin & Jieran Duan & Anyi Zhang, 2021. "Influencing Factors of Environmental Risk Perception during the COVID-19 Epidemic in China," IJERPH, MDPI, vol. 18(17), pages 1-21, September.
    16. Wu, Binrong & Wang, Lin & Zeng, Yu-Rong, 2022. "Interpretable wind speed prediction with multivariate time series and temporal fusion transformers," Energy, Elsevier, vol. 252(C).
    17. Wang, Cong & Zhang, Hongli & Fan, Wenhui & Ma, Ping, 2017. "A new chaotic time series hybrid prediction method of wind power based on EEMD-SE and full-parameters continued fraction," Energy, Elsevier, vol. 138(C), pages 977-990.
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