TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting Article Swipe
Songtao Huang
,
Zhen Zhao
,
Can Li
,
Lei Bai
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.06910
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.06910
Real-world time series often have multiple frequency components that are intertwined with each other, making accurate time series forecasting challenging. Decomposing the mixed frequency components into multiple single frequency components is a natural choice. However, the information density of patterns varies across different frequencies, and employing a uniform modeling approach for different frequency components can lead to inaccurate characterization. To address this challenges, inspired by the flexibility of the recent Kolmogorov-Arnold Network (KAN), we propose a KAN-based Frequency Decomposition Learning architecture (TimeKAN) to address the complex forecasting challenges caused by multiple frequency mixtures. Specifically, TimeKAN mainly consists of three components: Cascaded Frequency Decomposition (CFD) blocks, Multi-order KAN Representation Learning (M-KAN) blocks and Frequency Mixing blocks. CFD blocks adopt a bottom-up cascading approach to obtain series representations for each frequency band. Benefiting from the high flexibility of KAN, we design a novel M-KAN block to learn and represent specific temporal patterns within each frequency band. Finally, Frequency Mixing blocks is used to recombine the frequency bands into the original format. Extensive experimental results across multiple real-world time series datasets demonstrate that TimeKAN achieves state-of-the-art performance as an extremely lightweight architecture. Code is available at https://github.com/huangst21/TimeKAN.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2502.06910
- https://arxiv.org/pdf/2502.06910
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407423548
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4407423548Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2502.06910Digital Object Identifier
- Title
-
TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series ForecastingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-02-10Full publication date if available
- Authors
-
Songtao Huang, Zhen Zhao, Can Li, Lei BaiList of authors in order
- Landing page
-
https://arxiv.org/abs/2502.06910Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2502.06910Direct 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/2502.06910Direct OA link when available
- Concepts
-
Term (time), Decomposition, Series (stratigraphy), Architecture, Computer science, Time series, Artificial intelligence, Machine learning, Geography, Physics, Geology, Archaeology, Biology, Quantum mechanics, Paleontology, EcologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4407423548 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2502.06910 |
| ids.doi | https://doi.org/10.48550/arxiv.2502.06910 |
| ids.openalex | https://openalex.org/W4407423548 |
| fwci | |
| type | preprint |
| title | TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term 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 | 0.9757000207901001 |
| 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/T10320 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9350000023841858 |
| 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 | Neural Networks and Applications |
| 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 | 0.9078999757766724 |
| 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/C61797465 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7721801996231079 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q1188986 |
| concepts[0].display_name | Term (time) |
| concepts[1].id | https://openalex.org/C124681953 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6542778015136719 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q339062 |
| concepts[1].display_name | Decomposition |
| concepts[2].id | https://openalex.org/C143724316 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6478493213653564 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q312468 |
| concepts[2].display_name | Series (stratigraphy) |
| concepts[3].id | https://openalex.org/C123657996 |
| concepts[3].level | 2 |
| concepts[3].score | 0.6110472679138184 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q12271 |
| concepts[3].display_name | Architecture |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.4581150412559509 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C151406439 |
| concepts[5].level | 2 |
| concepts[5].score | 0.42897868156433105 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q186588 |
| concepts[5].display_name | Time series |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.3672049045562744 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C119857082 |
| concepts[7].level | 1 |
| concepts[7].score | 0.2798834443092346 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[7].display_name | Machine learning |
| concepts[8].id | https://openalex.org/C205649164 |
| concepts[8].level | 0 |
| concepts[8].score | 0.13971495628356934 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[8].display_name | Geography |
| concepts[9].id | https://openalex.org/C121332964 |
| concepts[9].level | 0 |
| concepts[9].score | 0.08284276723861694 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[9].display_name | Physics |
| concepts[10].id | https://openalex.org/C127313418 |
| concepts[10].level | 0 |
| concepts[10].score | 0.08002299070358276 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[10].display_name | Geology |
| concepts[11].id | https://openalex.org/C166957645 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q23498 |
| concepts[11].display_name | Archaeology |
| concepts[12].id | https://openalex.org/C86803240 |
| concepts[12].level | 0 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[12].display_name | Biology |
| concepts[13].id | https://openalex.org/C62520636 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[13].display_name | Quantum mechanics |
| 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 |
| concepts[15].id | https://openalex.org/C18903297 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7150 |
| concepts[15].display_name | Ecology |
| keywords[0].id | https://openalex.org/keywords/term |
| keywords[0].score | 0.7721801996231079 |
| keywords[0].display_name | Term (time) |
| keywords[1].id | https://openalex.org/keywords/decomposition |
| keywords[1].score | 0.6542778015136719 |
| keywords[1].display_name | Decomposition |
| keywords[2].id | https://openalex.org/keywords/series |
| keywords[2].score | 0.6478493213653564 |
| keywords[2].display_name | Series (stratigraphy) |
| keywords[3].id | https://openalex.org/keywords/architecture |
| keywords[3].score | 0.6110472679138184 |
| keywords[3].display_name | Architecture |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.4581150412559509 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/time-series |
| keywords[5].score | 0.42897868156433105 |
| keywords[5].display_name | Time series |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.3672049045562744 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/machine-learning |
| keywords[7].score | 0.2798834443092346 |
| keywords[7].display_name | Machine learning |
| keywords[8].id | https://openalex.org/keywords/geography |
| keywords[8].score | 0.13971495628356934 |
| keywords[8].display_name | Geography |
| keywords[9].id | https://openalex.org/keywords/physics |
| keywords[9].score | 0.08284276723861694 |
| keywords[9].display_name | Physics |
| keywords[10].id | https://openalex.org/keywords/geology |
| keywords[10].score | 0.08002299070358276 |
| keywords[10].display_name | Geology |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2502.06910 |
| 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/2502.06910 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| 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/2502.06910 |
| locations[1].id | doi:10.48550/arxiv.2502.06910 |
| 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.2502.06910 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5047433248 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-6325-9306 |
| authorships[0].author.display_name | Songtao Huang |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Huang, Songtao |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5072694516 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-0796-4078 |
| authorships[1].author.display_name | Zhen Zhao |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Zhao, Zhen |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5100334055 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-7110-6932 |
| authorships[2].author.display_name | Can Li |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Li, Can |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5100727433 |
| authorships[3].author.orcid | https://orcid.org/0009-0002-4409-3125 |
| authorships[3].author.display_name | Lei Bai |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Bai, Lei |
| authorships[3].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/2502.06910 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term 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 | 0.9757000207901001 |
| 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/W2980611886, https://openalex.org/W42295635, https://openalex.org/W1973996291, https://openalex.org/W1919101720, https://openalex.org/W2330575325, https://openalex.org/W2163803519, https://openalex.org/W2119012848, https://openalex.org/W2622688551, https://openalex.org/W1550175370, https://openalex.org/W1990205660 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2502.06910 |
| 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/2502.06910 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| 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/2502.06910 |
| primary_location.id | pmh:oai:arXiv.org:2502.06910 |
| 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/2502.06910 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| 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/2502.06910 |
| publication_date | 2025-02-10 |
| publication_year | 2025 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 31, 46, 75, 118, 139 |
| abstract_inverted_index.To | 59 |
| abstract_inverted_index.an | 185 |
| abstract_inverted_index.as | 184 |
| abstract_inverted_index.at | 192 |
| abstract_inverted_index.by | 64, 89 |
| abstract_inverted_index.is | 30, 158, 190 |
| abstract_inverted_index.of | 38, 67, 97, 135 |
| abstract_inverted_index.to | 56, 82, 122, 143, 160 |
| abstract_inverted_index.we | 73, 137 |
| abstract_inverted_index.CFD | 115 |
| abstract_inverted_index.KAN | 106 |
| abstract_inverted_index.and | 44, 111, 145 |
| abstract_inverted_index.are | 9 |
| abstract_inverted_index.can | 54 |
| abstract_inverted_index.for | 50, 126 |
| abstract_inverted_index.the | 21, 35, 65, 68, 84, 132, 162, 166 |
| abstract_inverted_index.Code | 189 |
| abstract_inverted_index.KAN, | 136 |
| abstract_inverted_index.each | 12, 127, 151 |
| abstract_inverted_index.from | 131 |
| abstract_inverted_index.have | 4 |
| abstract_inverted_index.high | 133 |
| abstract_inverted_index.into | 25, 165 |
| abstract_inverted_index.lead | 55 |
| abstract_inverted_index.that | 8, 179 |
| abstract_inverted_index.this | 61 |
| abstract_inverted_index.time | 1, 16, 175 |
| abstract_inverted_index.used | 159 |
| abstract_inverted_index.with | 11 |
| abstract_inverted_index.(CFD) | 103 |
| abstract_inverted_index.M-KAN | 141 |
| abstract_inverted_index.adopt | 117 |
| abstract_inverted_index.band. | 129, 153 |
| abstract_inverted_index.bands | 164 |
| abstract_inverted_index.block | 142 |
| abstract_inverted_index.learn | 144 |
| abstract_inverted_index.mixed | 22 |
| abstract_inverted_index.novel | 140 |
| abstract_inverted_index.often | 3 |
| abstract_inverted_index.three | 98 |
| abstract_inverted_index.(KAN), | 72 |
| abstract_inverted_index.Mixing | 113, 156 |
| abstract_inverted_index.across | 41, 172 |
| abstract_inverted_index.blocks | 110, 116, 157 |
| abstract_inverted_index.caused | 88 |
| abstract_inverted_index.design | 138 |
| abstract_inverted_index.mainly | 95 |
| abstract_inverted_index.making | 14 |
| abstract_inverted_index.obtain | 123 |
| abstract_inverted_index.other, | 13 |
| abstract_inverted_index.recent | 69 |
| abstract_inverted_index.series | 2, 17, 124, 176 |
| abstract_inverted_index.single | 27 |
| abstract_inverted_index.varies | 40 |
| abstract_inverted_index.within | 150 |
| abstract_inverted_index.(M-KAN) | 109 |
| abstract_inverted_index.Network | 71 |
| abstract_inverted_index.TimeKAN | 94, 180 |
| abstract_inverted_index.address | 60, 83 |
| abstract_inverted_index.blocks, | 104 |
| abstract_inverted_index.blocks. | 114 |
| abstract_inverted_index.choice. | 33 |
| abstract_inverted_index.complex | 85 |
| abstract_inverted_index.density | 37 |
| abstract_inverted_index.format. | 168 |
| abstract_inverted_index.natural | 32 |
| abstract_inverted_index.propose | 74 |
| abstract_inverted_index.results | 171 |
| abstract_inverted_index.uniform | 47 |
| abstract_inverted_index.Cascaded | 100 |
| abstract_inverted_index.Finally, | 154 |
| abstract_inverted_index.However, | 34 |
| abstract_inverted_index.Learning | 79, 108 |
| abstract_inverted_index.accurate | 15 |
| abstract_inverted_index.achieves | 181 |
| abstract_inverted_index.approach | 49, 121 |
| abstract_inverted_index.consists | 96 |
| abstract_inverted_index.datasets | 177 |
| abstract_inverted_index.inspired | 63 |
| abstract_inverted_index.modeling | 48 |
| abstract_inverted_index.multiple | 5, 26, 90, 173 |
| abstract_inverted_index.original | 167 |
| abstract_inverted_index.patterns | 39, 149 |
| abstract_inverted_index.specific | 147 |
| abstract_inverted_index.temporal | 148 |
| abstract_inverted_index.(TimeKAN) | 81 |
| abstract_inverted_index.Extensive | 169 |
| abstract_inverted_index.Frequency | 77, 101, 112, 155 |
| abstract_inverted_index.KAN-based | 76 |
| abstract_inverted_index.available | 191 |
| abstract_inverted_index.bottom-up | 119 |
| abstract_inverted_index.cascading | 120 |
| abstract_inverted_index.different | 42, 51 |
| abstract_inverted_index.employing | 45 |
| abstract_inverted_index.extremely | 186 |
| abstract_inverted_index.frequency | 6, 23, 28, 52, 91, 128, 152, 163 |
| abstract_inverted_index.mixtures. | 92 |
| abstract_inverted_index.recombine | 161 |
| abstract_inverted_index.represent | 146 |
| abstract_inverted_index.Benefiting | 130 |
| abstract_inverted_index.Real-world | 0 |
| abstract_inverted_index.challenges | 87 |
| abstract_inverted_index.components | 7, 24, 29, 53 |
| abstract_inverted_index.inaccurate | 57 |
| abstract_inverted_index.real-world | 174 |
| abstract_inverted_index.Decomposing | 20 |
| abstract_inverted_index.Multi-order | 105 |
| abstract_inverted_index.challenges, | 62 |
| abstract_inverted_index.components: | 99 |
| abstract_inverted_index.demonstrate | 178 |
| abstract_inverted_index.flexibility | 66, 134 |
| abstract_inverted_index.forecasting | 18, 86 |
| abstract_inverted_index.information | 36 |
| abstract_inverted_index.intertwined | 10 |
| abstract_inverted_index.lightweight | 187 |
| abstract_inverted_index.performance | 183 |
| abstract_inverted_index.architecture | 80 |
| abstract_inverted_index.challenging. | 19 |
| abstract_inverted_index.experimental | 170 |
| abstract_inverted_index.frequencies, | 43 |
| abstract_inverted_index.Decomposition | 78, 102 |
| abstract_inverted_index.Specifically, | 93 |
| abstract_inverted_index.architecture. | 188 |
| abstract_inverted_index.Representation | 107 |
| abstract_inverted_index.representations | 125 |
| abstract_inverted_index.state-of-the-art | 182 |
| abstract_inverted_index.Kolmogorov-Arnold | 70 |
| abstract_inverted_index.characterization. | 58 |
| abstract_inverted_index.https://github.com/huangst21/TimeKAN. | 193 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |