A Novel Nonferrous Metals Price Prediction Model Based on BiLSTM-ResNet with Grey Wolf Optimization and Wavelet Transform Article Swipe
Zhanglong Li
,
Yunlei Yang
,
Jiachun Zheng
,
Yang Wu
,
Yinghao Chen
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3233/faia231018
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3233/faia231018
Nonferrous metals are important commodities, and it is of great significance for policy makers and investors to accurately predict their price changes. Nevertheless, because the price of nonferrous metals present drastic fluctuations, developing a robust price prediction method is a tricky task. In this research, a hybrid model based on discrete wavelet transform (DWT), bidirectional long short-term memory (BiLSTM) and residual network (ResNet) is constructed for nonferrous metals price prediction. The hyper-parameters of the hybrid neural network are searched by grey wolf optimization (GWO) algorithm. Configuring reasonable parameters, which enhances the final prediction effect. Additionally, behind the second hidden layer, the low and high dimensional features are fused to prevent the degradation of the model. The original sequence is processed by DWT technology, then the sequence is reconstructed, which is beneficial to capture the essential trend. The experimental results show that the proposed BiLSTM-ResNet-GWO-DWT model is more accurate compared with the other benchmark models, which provides an effective reference significance.
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Metadata
- Type
- book-chapter
- Language
- en
- Landing Page
- https://doi.org/10.3233/faia231018
- https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA231018
- OA Status
- hybrid
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389762976
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389762976Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3233/faia231018Digital Object Identifier
- Title
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A Novel Nonferrous Metals Price Prediction Model Based on BiLSTM-ResNet with Grey Wolf Optimization and Wavelet TransformWork title
- Type
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book-chapterOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-12Full publication date if available
- Authors
-
Zhanglong Li, Yunlei Yang, Jiachun Zheng, Yang Wu, Yinghao ChenList of authors in order
- Landing page
-
https://doi.org/10.3233/faia231018Publisher landing page
- PDF URL
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https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA231018Direct link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://ebooks.iospress.nl/pdf/doi/10.3233/FAIA231018Direct OA link when available
- Concepts
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Residual, Discrete wavelet transform, Benchmark (surveying), Artificial neural network, Sequence (biology), Artificial intelligence, Wavelet transform, Computer science, Pattern recognition (psychology), Wavelet, Algorithm, Geography, Chemistry, Geodesy, BiochemistryTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2896018779, https://openalex.org/W2170529788, https://openalex.org/W1972732865, https://openalex.org/W2074173695, https://openalex.org/W2985123151, https://openalex.org/W2996345241, https://openalex.org/W3177806728, https://openalex.org/W2306547824, https://openalex.org/W2971907746, https://openalex.org/W2580977989, https://openalex.org/W4383820169, https://openalex.org/W2981224608, https://openalex.org/W4287879957, https://openalex.org/W3048494751, https://openalex.org/W3040674358, https://openalex.org/W4200548039, https://openalex.org/W4283644288, https://openalex.org/W3213310772, https://openalex.org/W3039642166, https://openalex.org/W2955168531, https://openalex.org/W3112298559, https://openalex.org/W2194775991, https://openalex.org/W3214377546, https://openalex.org/W2163605009, https://openalex.org/W3024836774, https://openalex.org/W2901625170, https://openalex.org/W2936404407, https://openalex.org/W4226530624, https://openalex.org/W2798074997, https://openalex.org/W3173615300, https://openalex.org/W2618530766 |
| referenced_works_count | 31 |
| abstract_inverted_index.a | 33, 39, 45 |
| abstract_inverted_index.In | 42 |
| abstract_inverted_index.an | 156 |
| abstract_inverted_index.by | 79, 120 |
| abstract_inverted_index.is | 7, 38, 63, 118, 126, 129, 145 |
| abstract_inverted_index.it | 6 |
| abstract_inverted_index.of | 8, 26, 72, 112 |
| abstract_inverted_index.on | 49 |
| abstract_inverted_index.to | 16, 108, 131 |
| abstract_inverted_index.DWT | 121 |
| abstract_inverted_index.The | 70, 115, 136 |
| abstract_inverted_index.and | 5, 14, 59, 102 |
| abstract_inverted_index.are | 2, 77, 106 |
| abstract_inverted_index.for | 11, 65 |
| abstract_inverted_index.low | 101 |
| abstract_inverted_index.the | 24, 73, 90, 96, 100, 110, 113, 124, 133, 141, 150 |
| abstract_inverted_index.grey | 80 |
| abstract_inverted_index.high | 103 |
| abstract_inverted_index.long | 55 |
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| abstract_inverted_index.show | 139 |
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| abstract_inverted_index.then | 123 |
| abstract_inverted_index.this | 43 |
| abstract_inverted_index.with | 149 |
| abstract_inverted_index.wolf | 81 |
| abstract_inverted_index.(GWO) | 83 |
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| abstract_inverted_index.final | 91 |
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| abstract_inverted_index.great | 9 |
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| abstract_inverted_index.price | 20, 25, 35, 68 |
| abstract_inverted_index.task. | 41 |
| abstract_inverted_index.their | 19 |
| abstract_inverted_index.which | 88, 128, 154 |
| abstract_inverted_index.(DWT), | 53 |
| abstract_inverted_index.behind | 95 |
| abstract_inverted_index.hidden | 98 |
| abstract_inverted_index.hybrid | 46, 74 |
| abstract_inverted_index.layer, | 99 |
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| abstract_inverted_index.memory | 57 |
| abstract_inverted_index.metals | 1, 28, 67 |
| abstract_inverted_index.method | 37 |
| abstract_inverted_index.model. | 114 |
| abstract_inverted_index.neural | 75 |
| abstract_inverted_index.policy | 12 |
| abstract_inverted_index.robust | 34 |
| abstract_inverted_index.second | 97 |
| abstract_inverted_index.trend. | 135 |
| abstract_inverted_index.tricky | 40 |
| abstract_inverted_index.because | 23 |
| abstract_inverted_index.capture | 132 |
| abstract_inverted_index.drastic | 30 |
| abstract_inverted_index.effect. | 93 |
| abstract_inverted_index.models, | 153 |
| abstract_inverted_index.network | 61, 76 |
| abstract_inverted_index.predict | 18 |
| abstract_inverted_index.present | 29 |
| abstract_inverted_index.prevent | 109 |
| abstract_inverted_index.results | 138 |
| abstract_inverted_index.wavelet | 51 |
| abstract_inverted_index.(BiLSTM) | 58 |
| abstract_inverted_index.(ResNet) | 62 |
| abstract_inverted_index.accurate | 147 |
| abstract_inverted_index.changes. | 21 |
| abstract_inverted_index.compared | 148 |
| abstract_inverted_index.discrete | 50 |
| abstract_inverted_index.enhances | 89 |
| abstract_inverted_index.features | 105 |
| abstract_inverted_index.original | 116 |
| abstract_inverted_index.proposed | 142 |
| abstract_inverted_index.provides | 155 |
| abstract_inverted_index.residual | 60 |
| abstract_inverted_index.searched | 78 |
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| abstract_inverted_index.benchmark | 152 |
| abstract_inverted_index.effective | 157 |
| abstract_inverted_index.essential | 134 |
| abstract_inverted_index.important | 3 |
| abstract_inverted_index.investors | 15 |
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| abstract_inverted_index.reference | 158 |
| abstract_inverted_index.research, | 44 |
| abstract_inverted_index.transform | 52 |
| abstract_inverted_index.Nonferrous | 0 |
| abstract_inverted_index.accurately | 17 |
| abstract_inverted_index.algorithm. | 84 |
| abstract_inverted_index.beneficial | 130 |
| abstract_inverted_index.developing | 32 |
| abstract_inverted_index.nonferrous | 27, 66 |
| abstract_inverted_index.prediction | 36, 92 |
| abstract_inverted_index.reasonable | 86 |
| abstract_inverted_index.short-term | 56 |
| abstract_inverted_index.Configuring | 85 |
| abstract_inverted_index.constructed | 64 |
| abstract_inverted_index.degradation | 111 |
| abstract_inverted_index.dimensional | 104 |
| abstract_inverted_index.parameters, | 87 |
| abstract_inverted_index.prediction. | 69 |
| abstract_inverted_index.technology, | 122 |
| abstract_inverted_index.commodities, | 4 |
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| abstract_inverted_index.optimization | 82 |
| abstract_inverted_index.significance | 10 |
| abstract_inverted_index.Additionally, | 94 |
| abstract_inverted_index.Nevertheless, | 22 |
| abstract_inverted_index.bidirectional | 54 |
| abstract_inverted_index.fluctuations, | 31 |
| abstract_inverted_index.significance. | 159 |
| abstract_inverted_index.reconstructed, | 127 |
| abstract_inverted_index.hyper-parameters | 71 |
| abstract_inverted_index.BiLSTM-ResNet-GWO-DWT | 143 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.68407072 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |