TimeCNN: Refining Cross-Variable Interaction on Time Point for Time Series Forecasting Article Swipe
Ao Hu
,
Dongkai Wang
,
Yong Dai
,
Shiyi Qi
,
Liangjian Wen
,
Jun Wang
,
Zhi Chen
,
Xun Zhou
,
Zenglin Xu
,
Jiang Duan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.04853
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.04853
Time series forecasting is extensively applied across diverse domains. Transformer-based models demonstrate significant potential in modeling cross-time and cross-variable interaction. However, we notice that the cross-variable correlation of multivariate time series demonstrates multifaceted (positive and negative correlations) and dynamic progression over time, which is not well captured by existing Transformer-based models. To address this issue, we propose a TimeCNN model to refine cross-variable interactions to enhance time series forecasting. Its key innovation is timepoint-independent, where each time point has an independent convolution kernel, allowing each time point to have its independent model to capture relationships among variables. This approach effectively handles both positive and negative correlations and adapts to the evolving nature of variable relationships over time. Extensive experiments conducted on 12 real-world datasets demonstrate that TimeCNN consistently outperforms state-of-the-art models. Notably, our model achieves significant reductions in computational requirements (approximately 60.46%) and parameter count (about 57.50%), while delivering inference speeds 3 to 4 times faster than the benchmark iTransformer model
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2410.04853
- https://arxiv.org/pdf/2410.04853
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403323913
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4403323913Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2410.04853Digital Object Identifier
- Title
-
TimeCNN: Refining Cross-Variable Interaction on Time Point for Time Series ForecastingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-10-07Full publication date if available
- Authors
-
Ao Hu, Dongkai Wang, Yong Dai, Shiyi Qi, Liangjian Wen, Jun Wang, Zhi Chen, Xun Zhou, Zenglin Xu, Jiang DuanList of authors in order
- Landing page
-
https://arxiv.org/abs/2410.04853Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2410.04853Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2410.04853Direct OA link when available
- Concepts
-
Refining (metallurgy), Variable (mathematics), Series (stratigraphy), Point (geometry), Time series, Computer science, Econometrics, Mathematics, Machine learning, Chemistry, Geology, Mathematical analysis, Physical chemistry, Geometry, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4403323913 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2410.04853 |
| ids.doi | https://doi.org/10.48550/arxiv.2410.04853 |
| ids.openalex | https://openalex.org/W4403323913 |
| fwci | |
| type | preprint |
| title | TimeCNN: Refining Cross-Variable Interaction on Time Point for Time Series Forecasting |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12205 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9983000159263611 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1711 |
| topics[0].subfield.display_name | Signal Processing |
| topics[0].display_name | Time Series Analysis and Forecasting |
| topics[1].id | https://openalex.org/T13083 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9404000043869019 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Advanced Text Analysis Techniques |
| topics[2].id | https://openalex.org/T11326 |
| topics[2].field.id | https://openalex.org/fields/18 |
| topics[2].field.display_name | Decision Sciences |
| topics[2].score | 0.9329000115394592 |
| topics[2].domain.id | https://openalex.org/domains/2 |
| topics[2].domain.display_name | Social Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1803 |
| topics[2].subfield.display_name | Management Science and Operations Research |
| topics[2].display_name | Stock Market Forecasting Methods |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C60044698 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7218295335769653 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1283324 |
| concepts[0].display_name | Refining (metallurgy) |
| concepts[1].id | https://openalex.org/C182365436 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6846250891685486 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q50701 |
| concepts[1].display_name | Variable (mathematics) |
| concepts[2].id | https://openalex.org/C143724316 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6577698588371277 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q312468 |
| concepts[2].display_name | Series (stratigraphy) |
| concepts[3].id | https://openalex.org/C28719098 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5011188983917236 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q44946 |
| concepts[3].display_name | Point (geometry) |
| concepts[4].id | https://openalex.org/C151406439 |
| concepts[4].level | 2 |
| concepts[4].score | 0.46653181314468384 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q186588 |
| concepts[4].display_name | Time series |
| concepts[5].id | https://openalex.org/C41008148 |
| concepts[5].level | 0 |
| concepts[5].score | 0.44746312499046326 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[5].display_name | Computer science |
| concepts[6].id | https://openalex.org/C149782125 |
| concepts[6].level | 1 |
| concepts[6].score | 0.3263618052005768 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[6].display_name | Econometrics |
| concepts[7].id | https://openalex.org/C33923547 |
| concepts[7].level | 0 |
| concepts[7].score | 0.2511831820011139 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[7].display_name | Mathematics |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.1839645802974701 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C185592680 |
| concepts[9].level | 0 |
| concepts[9].score | 0.10229358077049255 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[9].display_name | Chemistry |
| concepts[10].id | https://openalex.org/C127313418 |
| concepts[10].level | 0 |
| concepts[10].score | 0.05397343635559082 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[10].display_name | Geology |
| concepts[11].id | https://openalex.org/C134306372 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[11].display_name | Mathematical analysis |
| concepts[12].id | https://openalex.org/C147789679 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11372 |
| concepts[12].display_name | Physical chemistry |
| concepts[13].id | https://openalex.org/C2524010 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[13].display_name | Geometry |
| concepts[14].id | https://openalex.org/C151730666 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7205 |
| concepts[14].display_name | Paleontology |
| keywords[0].id | https://openalex.org/keywords/refining |
| keywords[0].score | 0.7218295335769653 |
| keywords[0].display_name | Refining (metallurgy) |
| keywords[1].id | https://openalex.org/keywords/variable |
| keywords[1].score | 0.6846250891685486 |
| keywords[1].display_name | Variable (mathematics) |
| keywords[2].id | https://openalex.org/keywords/series |
| keywords[2].score | 0.6577698588371277 |
| keywords[2].display_name | Series (stratigraphy) |
| keywords[3].id | https://openalex.org/keywords/point |
| keywords[3].score | 0.5011188983917236 |
| keywords[3].display_name | Point (geometry) |
| keywords[4].id | https://openalex.org/keywords/time-series |
| keywords[4].score | 0.46653181314468384 |
| keywords[4].display_name | Time series |
| keywords[5].id | https://openalex.org/keywords/computer-science |
| keywords[5].score | 0.44746312499046326 |
| keywords[5].display_name | Computer science |
| keywords[6].id | https://openalex.org/keywords/econometrics |
| keywords[6].score | 0.3263618052005768 |
| keywords[6].display_name | Econometrics |
| keywords[7].id | https://openalex.org/keywords/mathematics |
| keywords[7].score | 0.2511831820011139 |
| keywords[7].display_name | Mathematics |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.1839645802974701 |
| keywords[8].display_name | Machine learning |
| keywords[9].id | https://openalex.org/keywords/chemistry |
| keywords[9].score | 0.10229358077049255 |
| keywords[9].display_name | Chemistry |
| keywords[10].id | https://openalex.org/keywords/geology |
| keywords[10].score | 0.05397343635559082 |
| keywords[10].display_name | Geology |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2410.04853 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2410.04853 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2410.04853 |
| locations[1].id | doi:10.48550/arxiv.2410.04853 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2410.04853 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5024260035 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Ao Hu |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Hu, Ao |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5113429452 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Dongkai Wang |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Wang, Dongkai |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5040569295 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-1804-1778 |
| authorships[2].author.display_name | Yong Dai |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Dai, Yong |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5022820323 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-8060-1273 |
| authorships[3].author.display_name | Shiyi Qi |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Qi, Shiyi |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5011356958 |
| authorships[4].author.orcid | https://orcid.org/0009-0000-3493-6403 |
| authorships[4].author.display_name | Liangjian Wen |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Wen, Liangjian |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5100384682 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-0926-4761 |
| authorships[5].author.display_name | Jun Wang |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Wang, Jun |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5115596320 |
| authorships[6].author.orcid | https://orcid.org/0009-0000-5387-8376 |
| authorships[6].author.display_name | Zhi Chen |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Chen, Zhi |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5086198510 |
| authorships[7].author.orcid | https://orcid.org/0000-0003-4930-6572 |
| authorships[7].author.display_name | Xun Zhou |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Zhou, Xun |
| authorships[7].is_corresponding | False |
| authorships[8].author.id | https://openalex.org/A5051227924 |
| authorships[8].author.orcid | https://orcid.org/0000-0001-5550-6461 |
| authorships[8].author.display_name | Zenglin Xu |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Xu, Zenglin |
| authorships[8].is_corresponding | False |
| authorships[9].author.id | https://openalex.org/A5101518907 |
| authorships[9].author.orcid | https://orcid.org/0000-0002-8573-311X |
| authorships[9].author.display_name | Jiang Duan |
| authorships[9].author_position | last |
| authorships[9].raw_author_name | Duan, Jiang |
| authorships[9].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2410.04853 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | TimeCNN: Refining Cross-Variable Interaction on Time Point for Time Series Forecasting |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T12205 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9983000159263611 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1711 |
| primary_topic.subfield.display_name | Signal Processing |
| primary_topic.display_name | Time Series Analysis and Forecasting |
| related_works | https://openalex.org/W1595345252, https://openalex.org/W2392526918, https://openalex.org/W2362540361, https://openalex.org/W2019560916, https://openalex.org/W2361983698, https://openalex.org/W2347697528, https://openalex.org/W2119012848, https://openalex.org/W2622688551, https://openalex.org/W1550175370, https://openalex.org/W1990205660 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2410.04853 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2410.04853 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2410.04853 |
| primary_location.id | pmh:oai:arXiv.org:2410.04853 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2410.04853 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2410.04853 |
| publication_date | 2024-10-07 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.3 | 151 |
| abstract_inverted_index.4 | 153 |
| abstract_inverted_index.a | 57 |
| abstract_inverted_index.12 | 121 |
| abstract_inverted_index.To | 51 |
| abstract_inverted_index.an | 79 |
| abstract_inverted_index.by | 47 |
| abstract_inverted_index.in | 14, 137 |
| abstract_inverted_index.is | 3, 43, 72 |
| abstract_inverted_index.of | 27, 112 |
| abstract_inverted_index.on | 120 |
| abstract_inverted_index.to | 60, 64, 87, 92, 108, 152 |
| abstract_inverted_index.we | 21, 55 |
| abstract_inverted_index.Its | 69 |
| abstract_inverted_index.and | 17, 34, 37, 103, 106, 142 |
| abstract_inverted_index.has | 78 |
| abstract_inverted_index.its | 89 |
| abstract_inverted_index.key | 70 |
| abstract_inverted_index.not | 44 |
| abstract_inverted_index.our | 132 |
| abstract_inverted_index.the | 24, 109, 157 |
| abstract_inverted_index.This | 97 |
| abstract_inverted_index.Time | 0 |
| abstract_inverted_index.both | 101 |
| abstract_inverted_index.each | 75, 84 |
| abstract_inverted_index.have | 88 |
| abstract_inverted_index.over | 40, 115 |
| abstract_inverted_index.than | 156 |
| abstract_inverted_index.that | 23, 125 |
| abstract_inverted_index.this | 53 |
| abstract_inverted_index.time | 29, 66, 76, 85 |
| abstract_inverted_index.well | 45 |
| abstract_inverted_index.among | 95 |
| abstract_inverted_index.count | 144 |
| abstract_inverted_index.model | 59, 91, 133, 160 |
| abstract_inverted_index.point | 77, 86 |
| abstract_inverted_index.time, | 41 |
| abstract_inverted_index.time. | 116 |
| abstract_inverted_index.times | 154 |
| abstract_inverted_index.where | 74 |
| abstract_inverted_index.which | 42 |
| abstract_inverted_index.while | 147 |
| abstract_inverted_index.(about | 145 |
| abstract_inverted_index.across | 6 |
| abstract_inverted_index.adapts | 107 |
| abstract_inverted_index.faster | 155 |
| abstract_inverted_index.issue, | 54 |
| abstract_inverted_index.models | 10 |
| abstract_inverted_index.nature | 111 |
| abstract_inverted_index.notice | 22 |
| abstract_inverted_index.refine | 61 |
| abstract_inverted_index.series | 1, 30, 67 |
| abstract_inverted_index.speeds | 150 |
| abstract_inverted_index.60.46%) | 141 |
| abstract_inverted_index.TimeCNN | 58, 126 |
| abstract_inverted_index.address | 52 |
| abstract_inverted_index.applied | 5 |
| abstract_inverted_index.capture | 93 |
| abstract_inverted_index.diverse | 7 |
| abstract_inverted_index.dynamic | 38 |
| abstract_inverted_index.enhance | 65 |
| abstract_inverted_index.handles | 100 |
| abstract_inverted_index.kernel, | 82 |
| abstract_inverted_index.models. | 50, 130 |
| abstract_inverted_index.propose | 56 |
| abstract_inverted_index.57.50%), | 146 |
| abstract_inverted_index.However, | 20 |
| abstract_inverted_index.Notably, | 131 |
| abstract_inverted_index.achieves | 134 |
| abstract_inverted_index.allowing | 83 |
| abstract_inverted_index.approach | 98 |
| abstract_inverted_index.captured | 46 |
| abstract_inverted_index.datasets | 123 |
| abstract_inverted_index.domains. | 8 |
| abstract_inverted_index.evolving | 110 |
| abstract_inverted_index.existing | 48 |
| abstract_inverted_index.modeling | 15 |
| abstract_inverted_index.negative | 35, 104 |
| abstract_inverted_index.positive | 102 |
| abstract_inverted_index.variable | 113 |
| abstract_inverted_index.(positive | 33 |
| abstract_inverted_index.Extensive | 117 |
| abstract_inverted_index.benchmark | 158 |
| abstract_inverted_index.conducted | 119 |
| abstract_inverted_index.inference | 149 |
| abstract_inverted_index.parameter | 143 |
| abstract_inverted_index.potential | 13 |
| abstract_inverted_index.cross-time | 16 |
| abstract_inverted_index.delivering | 148 |
| abstract_inverted_index.innovation | 71 |
| abstract_inverted_index.real-world | 122 |
| abstract_inverted_index.reductions | 136 |
| abstract_inverted_index.variables. | 96 |
| abstract_inverted_index.convolution | 81 |
| abstract_inverted_index.correlation | 26 |
| abstract_inverted_index.demonstrate | 11, 124 |
| abstract_inverted_index.effectively | 99 |
| abstract_inverted_index.experiments | 118 |
| abstract_inverted_index.extensively | 4 |
| abstract_inverted_index.forecasting | 2 |
| abstract_inverted_index.independent | 80, 90 |
| abstract_inverted_index.outperforms | 128 |
| abstract_inverted_index.progression | 39 |
| abstract_inverted_index.significant | 12, 135 |
| abstract_inverted_index.consistently | 127 |
| abstract_inverted_index.correlations | 105 |
| abstract_inverted_index.demonstrates | 31 |
| abstract_inverted_index.forecasting. | 68 |
| abstract_inverted_index.iTransformer | 159 |
| abstract_inverted_index.interaction. | 19 |
| abstract_inverted_index.interactions | 63 |
| abstract_inverted_index.multifaceted | 32 |
| abstract_inverted_index.multivariate | 28 |
| abstract_inverted_index.requirements | 139 |
| abstract_inverted_index.computational | 138 |
| abstract_inverted_index.correlations) | 36 |
| abstract_inverted_index.relationships | 94, 114 |
| abstract_inverted_index.(approximately | 140 |
| abstract_inverted_index.cross-variable | 18, 25, 62 |
| abstract_inverted_index.state-of-the-art | 129 |
| abstract_inverted_index.Transformer-based | 9, 49 |
| abstract_inverted_index.timepoint-independent, | 73 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile |