Double-Path Adaptive-correlation Spatial-Temporal Inverted Transformer for Stock Time Series Forecasting Article Swipe
Wenbo Yan
,
Ying Tan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.15662
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.15662
Spatial-temporal graph neural networks (STGNNs) have achieved significant success in various time series forecasting tasks. However, due to the lack of explicit and fixed spatial relationships in stock prediction tasks, many STGNNs fail to perform effectively in this domain. While some STGNNs learn spatial relationships from time series, they often lack comprehensiveness. Research indicates that modeling time series using feature changes as tokens reveals entirely different information compared to using time steps as tokens. To more comprehensively extract dynamic spatial information from stock data, we propose a Double-Path Adaptive-correlation Spatial-Temporal Inverted Transformer (DPA-STIFormer). DPA-STIFormer models each node via continuous changes in features as tokens and introduces a Double Direction Self-adaptation Fusion mechanism. This mechanism decomposes node encoding into temporal and feature representations, simultaneously extracting different spatial correlations from a double path approach, and proposes a Double-path gating mechanism to fuse these two types of correlation information. Experiments conducted on four stock market datasets demonstrate state-of-the-art results, validating the model's superior capability in uncovering latent temporal-correlation patterns.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2409.15662
- https://arxiv.org/pdf/2409.15662
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403786544
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- OpenAlex ID
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https://openalex.org/W4403786544Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2409.15662Digital Object Identifier
- Title
-
Double-Path Adaptive-correlation Spatial-Temporal Inverted Transformer for Stock Time Series ForecastingWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-24Full publication date if available
- Authors
-
Wenbo Yan, Ying TanList of authors in order
- Landing page
-
https://arxiv.org/abs/2409.15662Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2409.15662Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2409.15662Direct OA link when available
- Concepts
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Computer science, Series (stratigraphy), Stock (firearms), Path (computing), Time series, Transformer, Spatial correlation, Econometrics, Algorithm, Geography, Mathematics, Geology, Engineering, Machine learning, Telecommunications, Electrical engineering, Paleontology, Programming language, Archaeology, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.mechanism | 113, 137 |
| abstract_inverted_index.patterns. | 165 |
| abstract_inverted_index.capability | 160 |
| abstract_inverted_index.continuous | 98 |
| abstract_inverted_index.decomposes | 114 |
| abstract_inverted_index.extracting | 123 |
| abstract_inverted_index.introduces | 105 |
| abstract_inverted_index.mechanism. | 111 |
| abstract_inverted_index.prediction | 28 |
| abstract_inverted_index.uncovering | 162 |
| abstract_inverted_index.validating | 156 |
| abstract_inverted_index.Double-Path | 87 |
| abstract_inverted_index.Double-path | 135 |
| abstract_inverted_index.Experiments | 146 |
| abstract_inverted_index.Transformer | 91 |
| abstract_inverted_index.correlation | 144 |
| abstract_inverted_index.demonstrate | 153 |
| abstract_inverted_index.effectively | 35 |
| abstract_inverted_index.forecasting | 13 |
| abstract_inverted_index.information | 66, 80 |
| abstract_inverted_index.significant | 7 |
| abstract_inverted_index.correlations | 126 |
| abstract_inverted_index.information. | 145 |
| abstract_inverted_index.DPA-STIFormer | 93 |
| abstract_inverted_index.relationships | 25, 44 |
| abstract_inverted_index.simultaneously | 122 |
| abstract_inverted_index.Self-adaptation | 109 |
| abstract_inverted_index.comprehensively | 76 |
| abstract_inverted_index.(DPA-STIFormer). | 92 |
| abstract_inverted_index.Spatial-Temporal | 89 |
| abstract_inverted_index.Spatial-temporal | 0 |
| abstract_inverted_index.representations, | 121 |
| abstract_inverted_index.state-of-the-art | 154 |
| abstract_inverted_index.comprehensiveness. | 51 |
| abstract_inverted_index.Adaptive-correlation | 88 |
| abstract_inverted_index.temporal-correlation | 164 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile |