Forecasting Crude Oil Prices: a Deep Learning based Model Article Swipe
Yanhui Chen
,
Kaijian He
,
Kwok Fai Tso
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2017.11.373
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1016/j.procs.2017.11.373
With the popularity of the deep learning model in the engineering fields, it has attracted significant research interests in the economic and finance fields. In this paper, we use the deep learning model to capture the unknown complex nonlinear characteristics of the crude oil price movement. We further propose a new hybrid crude oil price forecasting model based on the deep learning model. Using the proposed model, major crude oil price movement is analyzed and modeled. The performance of the proposed model is evaluated using the price data in the WTI crude oil markets. The empirical results show that the proposed model achieves the improved forecasting accuracy.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.procs.2017.11.373
- OA Status
- diamond
- Cited By
- 117
- References
- 18
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W2771794792Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.procs.2017.11.373Digital Object Identifier
- Title
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Forecasting Crude Oil Prices: a Deep Learning based ModelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
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2017-01-01Full publication date if available
- Authors
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Yanhui Chen, Kaijian He, Kwok Fai TsoList of authors in order
- Landing page
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https://doi.org/10.1016/j.procs.2017.11.373Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.procs.2017.11.373Direct OA link when available
- Concepts
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Crude oil, Computer science, Deep learning, West Texas Intermediate, Artificial intelligence, Popularity, Machine learning, Petroleum engineering, Social psychology, Psychology, EngineeringTop concepts (fields/topics) attached by OpenAlex
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117Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 19, 2023: 27, 2022: 15, 2021: 13Per-year citation counts (last 5 years)
- References (count)
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18Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.achieves | 102 |
| abstract_inverted_index.analyzed | 73 |
| abstract_inverted_index.economic | 20 |
| abstract_inverted_index.improved | 104 |
| abstract_inverted_index.learning | 6, 31, 61 |
| abstract_inverted_index.markets. | 93 |
| abstract_inverted_index.modeled. | 75 |
| abstract_inverted_index.movement | 71 |
| abstract_inverted_index.proposed | 65, 80, 100 |
| abstract_inverted_index.research | 16 |
| abstract_inverted_index.accuracy. | 106 |
| abstract_inverted_index.attracted | 14 |
| abstract_inverted_index.empirical | 95 |
| abstract_inverted_index.evaluated | 83 |
| abstract_inverted_index.interests | 17 |
| abstract_inverted_index.movement. | 45 |
| abstract_inverted_index.nonlinear | 38 |
| abstract_inverted_index.popularity | 2 |
| abstract_inverted_index.engineering | 10 |
| abstract_inverted_index.forecasting | 55, 105 |
| abstract_inverted_index.performance | 77 |
| abstract_inverted_index.significant | 15 |
| abstract_inverted_index.characteristics | 39 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| corresponding_author_ids | https://openalex.org/A5028351142 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I121296143, https://openalex.org/I75390827 |
| citation_normalized_percentile.value | 0.99619751 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |