A multisource data‐driven combined forecasting model based on internet search keyword screening method for interval soybean futures price Article Swipe
Rui Luo
,
Jinpei Liu
,
Piao Wang
,
Zhifu Tao
,
Huayou Chen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/for.3035
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/for.3035
Accurate soybean futures price prediction is critical to related agricultural production, warehousing, and trading. Interval forecasting can avoid the loss of fluctuation information and evaluate the uncertainty of futures prices. However, most previous studies only consider the single‐type auxiliary variable, which will cause the deficiency of valued information. Moreover, the research concentrating on internet search index ignores the search habits of investors, resulting in subjectivity in keyword selection. Therefore, a novel multisource data‐driven combined forecasting model is proposed that consists of four parts: unstructured data processing, interval multi‐scale decomposition, interval combination forecasting, and model evaluation. First, sentiment analysis technology is used to convert news text into sentiment scores. The internet search keyword screening method based on latent Dirichlet allocation is then constructed to achieve the quantification of investor attention. Second, a decomposition method is applied to decompose the original interval‐valued series into finite more stationary components. Third, interval prediction results are obtained by machine learning‐based multiple predictors. Finally, the model evaluation module comprising error evaluation indicators and comparison experiments is presented to verify the effectiveness. The experimental results show that the proposed model has higher prediction accuracy, which indicates that multisource data and designed keyword screening methods can enhance the forecasting performance of the model.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/for.3035
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/for.3035
- OA Status
- bronze
- Cited By
- 15
- References
- 51
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387220092
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387220092Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/for.3035Digital Object Identifier
- Title
-
A multisource data‐driven combined forecasting model based on internet search keyword screening method for interval soybean futures priceWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-09-29Full publication date if available
- Authors
-
Rui Luo, Jinpei Liu, Piao Wang, Zhifu Tao, Huayou ChenList of authors in order
- Landing page
-
https://doi.org/10.1002/for.3035Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/for.3035Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/for.3035Direct OA link when available
- Concepts
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Computer science, Interval (graph theory), Futures contract, Data mining, Latent Dirichlet allocation, Probabilistic forecasting, Machine learning, Artificial intelligence, Econometrics, Topic model, Mathematics, Probabilistic logic, Financial economics, Combinatorics, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 10Per-year citation counts (last 5 years)
- References (count)
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51Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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