Deep Time Series Forecasting Models: A Comprehensive Survey Article Swipe
Deep learning, a crucial technique for achieving artificial intelligence (AI), has been successfully applied in many fields. The gradual application of the latest architectures of deep learning in the field of time series forecasting (TSF), such as Transformers, has shown excellent performance and results compared to traditional statistical methods. These applications are widely present in academia and in our daily lives, covering many areas including forecasting electricity consumption in power systems, meteorological rainfall, traffic flow, quantitative trading, risk control in finance, sales operations and price predictions for commercial companies, and pandemic prediction in the medical field. Deep learning-based TSF tasks stand out as one of the most valuable AI scenarios for research, playing an important role in explaining complex real-world phenomena. However, deep learning models still face challenges: they need to deal with the challenge of large-scale data in the information age, achieve longer forecasting ranges, reduce excessively high computational complexity, etc. Therefore, novel methods and more effective solutions are essential. In this paper, we review the latest developments in deep learning for TSF. We begin by introducing the recent development trends in the field of TSF and then propose a new taxonomy from the perspective of deep neural network models, comprehensively covering articles published over the past five years. We also organize commonly used experimental evaluation metrics and datasets. Finally, we point out current issues with the existing solutions and suggest promising future directions in the field of deep learning combined with TSF. This paper is the most comprehensive review related to TSF in recent years and will provide a detailed index for researchers in this field and those who are just starting out.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/math12101504
- https://www.mdpi.com/2227-7390/12/10/1504/pdf?version=1715582365
- OA Status
- gold
- Cited By
- 61
- References
- 90
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396863907
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4396863907Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/math12101504Digital Object Identifier
- Title
-
Deep Time Series Forecasting Models: A Comprehensive SurveyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-11Full publication date if available
- Authors
-
Xinhe Liu, Wenmin WangList of authors in order
- Landing page
-
https://doi.org/10.3390/math12101504Publisher landing page
- PDF URL
-
https://www.mdpi.com/2227-7390/12/10/1504/pdf?version=1715582365Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2227-7390/12/10/1504/pdf?version=1715582365Direct OA link when available
- Concepts
-
Series (stratigraphy), Time series, Computer science, Data science, Econometrics, Machine learning, Mathematics, Geology, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
61Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 50, 2024: 11Per-year citation counts (last 5 years)
- References (count)
-
90Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4396863907 |
|---|---|
| doi | https://doi.org/10.3390/math12101504 |
| ids.doi | https://doi.org/10.3390/math12101504 |
| ids.openalex | https://openalex.org/W4396863907 |
| fwci | 43.46276018 |
| type | article |
| title | Deep Time Series Forecasting Models: A Comprehensive Survey |
| biblio.issue | 10 |
| biblio.volume | 12 |
| biblio.last_page | 1504 |
| biblio.first_page | 1504 |
| 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 | 0.9991999864578247 |
| 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/T11326 |
| topics[1].field.id | https://openalex.org/fields/18 |
| topics[1].field.display_name | Decision Sciences |
| topics[1].score | 0.996999979019165 |
| topics[1].domain.id | https://openalex.org/domains/2 |
| topics[1].domain.display_name | Social Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1803 |
| topics[1].subfield.display_name | Management Science and Operations Research |
| topics[1].display_name | Stock Market Forecasting Methods |
| topics[2].id | https://openalex.org/T11918 |
| topics[2].field.id | https://openalex.org/fields/18 |
| topics[2].field.display_name | Decision Sciences |
| topics[2].score | 0.9937000274658203 |
| 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 | Forecasting Techniques and Applications |
| is_xpac | False |
| apc_list.value | 1800 |
| apc_list.currency | CHF |
| apc_list.value_usd | 1949 |
| apc_paid.value | 1800 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 1949 |
| concepts[0].id | https://openalex.org/C143724316 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6308179497718811 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q312468 |
| concepts[0].display_name | Series (stratigraphy) |
| concepts[1].id | https://openalex.org/C151406439 |
| concepts[1].level | 2 |
| concepts[1].score | 0.4755370616912842 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q186588 |
| concepts[1].display_name | Time series |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.41635534167289734 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C2522767166 |
| concepts[3].level | 1 |
| concepts[3].score | 0.36074918508529663 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2374463 |
| concepts[3].display_name | Data science |
| concepts[4].id | https://openalex.org/C149782125 |
| concepts[4].level | 1 |
| concepts[4].score | 0.3491213321685791 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[4].display_name | Econometrics |
| concepts[5].id | https://openalex.org/C119857082 |
| concepts[5].level | 1 |
| concepts[5].score | 0.21607014536857605 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[5].display_name | Machine learning |
| concepts[6].id | https://openalex.org/C33923547 |
| concepts[6].level | 0 |
| concepts[6].score | 0.164212167263031 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[6].display_name | Mathematics |
| concepts[7].id | https://openalex.org/C127313418 |
| concepts[7].level | 0 |
| concepts[7].score | 0.11176547408103943 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[7].display_name | Geology |
| concepts[8].id | https://openalex.org/C151730666 |
| concepts[8].level | 1 |
| concepts[8].score | 0.0 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7205 |
| concepts[8].display_name | Paleontology |
| keywords[0].id | https://openalex.org/keywords/series |
| keywords[0].score | 0.6308179497718811 |
| keywords[0].display_name | Series (stratigraphy) |
| keywords[1].id | https://openalex.org/keywords/time-series |
| keywords[1].score | 0.4755370616912842 |
| keywords[1].display_name | Time series |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.41635534167289734 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/data-science |
| keywords[3].score | 0.36074918508529663 |
| keywords[3].display_name | Data science |
| keywords[4].id | https://openalex.org/keywords/econometrics |
| keywords[4].score | 0.3491213321685791 |
| keywords[4].display_name | Econometrics |
| keywords[5].id | https://openalex.org/keywords/machine-learning |
| keywords[5].score | 0.21607014536857605 |
| keywords[5].display_name | Machine learning |
| keywords[6].id | https://openalex.org/keywords/mathematics |
| keywords[6].score | 0.164212167263031 |
| keywords[6].display_name | Mathematics |
| keywords[7].id | https://openalex.org/keywords/geology |
| keywords[7].score | 0.11176547408103943 |
| keywords[7].display_name | Geology |
| language | en |
| locations[0].id | doi:10.3390/math12101504 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210192031 |
| locations[0].source.issn | 2227-7390 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2227-7390 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Mathematics |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/2227-7390/12/10/1504/pdf?version=1715582365 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Mathematics |
| locations[0].landing_page_url | https://doi.org/10.3390/math12101504 |
| locations[1].id | pmh:oai:doaj.org/article:a430296f40d94bcc9d60e9e54386e13e |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Mathematics, Vol 12, Iss 10, p 1504 (2024) |
| locations[1].landing_page_url | https://doaj.org/article/a430296f40d94bcc9d60e9e54386e13e |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5072104333 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Xinhe Liu |
| authorships[0].countries | MO |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I111950717 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Computer Science and Engineering, Macau University of Science and Technology, Macao 999078, China |
| authorships[0].institutions[0].id | https://openalex.org/I111950717 |
| authorships[0].institutions[0].ror | https://ror.org/03jqs2n27 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I111950717, https://openalex.org/I4391767947 |
| authorships[0].institutions[0].country_code | MO |
| authorships[0].institutions[0].display_name | Macau University of Science and Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Xinhe Liu |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Computer Science and Engineering, Macau University of Science and Technology, Macao 999078, China |
| authorships[1].author.id | https://openalex.org/A5017052768 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-2664-4413 |
| authorships[1].author.display_name | Wenmin Wang |
| authorships[1].countries | MO |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I111950717 |
| authorships[1].affiliations[0].raw_affiliation_string | School of Computer Science and Engineering, Macau University of Science and Technology, Macao 999078, China |
| authorships[1].institutions[0].id | https://openalex.org/I111950717 |
| authorships[1].institutions[0].ror | https://ror.org/03jqs2n27 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I111950717, https://openalex.org/I4391767947 |
| authorships[1].institutions[0].country_code | MO |
| authorships[1].institutions[0].display_name | Macau University of Science and Technology |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | Wenmin Wang |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | School of Computer Science and Engineering, Macau University of Science and Technology, Macao 999078, China |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/2227-7390/12/10/1504/pdf?version=1715582365 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Deep Time Series Forecasting Models: A Comprehensive Survey |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| 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 | 0.9991999864578247 |
| 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/W4391375266, https://openalex.org/W2748952813, https://openalex.org/W2390279801, https://openalex.org/W2358668433, https://openalex.org/W4396701345, https://openalex.org/W2376932109, https://openalex.org/W2001405890, https://openalex.org/W2622688551, https://openalex.org/W1550175370, https://openalex.org/W1990205660 |
| cited_by_count | 61 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 50 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 11 |
| locations_count | 2 |
| best_oa_location.id | doi:10.3390/math12101504 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210192031 |
| best_oa_location.source.issn | 2227-7390 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2227-7390 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Mathematics |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/2227-7390/12/10/1504/pdf?version=1715582365 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Mathematics |
| best_oa_location.landing_page_url | https://doi.org/10.3390/math12101504 |
| primary_location.id | doi:10.3390/math12101504 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210192031 |
| primary_location.source.issn | 2227-7390 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2227-7390 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Mathematics |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2227-7390/12/10/1504/pdf?version=1715582365 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Mathematics |
| primary_location.landing_page_url | https://doi.org/10.3390/math12101504 |
| publication_date | 2024-05-11 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2980994438, https://openalex.org/W3022367555, https://openalex.org/W3211539727, https://openalex.org/W2019459021, https://openalex.org/W2747599906, https://openalex.org/W2064675550, https://openalex.org/W3171884590, https://openalex.org/W2157331557, https://openalex.org/W3177318507, https://openalex.org/W6737778391, https://openalex.org/W2567070169, https://openalex.org/W2163605009, https://openalex.org/W2423557781, https://openalex.org/W2510642588, https://openalex.org/W6797297377, https://openalex.org/W4378574837, https://openalex.org/W4381245607, https://openalex.org/W4382203079, https://openalex.org/W4223929908, https://openalex.org/W6795140394, https://openalex.org/W4285579895, https://openalex.org/W6727651223, https://openalex.org/W6797155008, https://openalex.org/W4225494949, https://openalex.org/W6889955440, https://openalex.org/W2604847698, https://openalex.org/W2613328025, https://openalex.org/W2808955427, https://openalex.org/W2962752580, https://openalex.org/W2949468773, https://openalex.org/W6859769344, https://openalex.org/W2988226917, https://openalex.org/W2996062451, https://openalex.org/W4303438998, https://openalex.org/W2954731415, https://openalex.org/W6784835917, https://openalex.org/W4372342073, https://openalex.org/W4280531713, https://openalex.org/W6846376059, https://openalex.org/W4290945834, https://openalex.org/W4386253606, https://openalex.org/W4295124107, https://openalex.org/W4385270114, https://openalex.org/W4395468431, https://openalex.org/W4396212703, https://openalex.org/W4387846390, https://openalex.org/W4391621245, https://openalex.org/W4383752905, https://openalex.org/W4387848755, https://openalex.org/W4385565515, https://openalex.org/W4388184870, https://openalex.org/W4388514908, https://openalex.org/W3154291377, https://openalex.org/W6810023484, https://openalex.org/W4378945214, https://openalex.org/W6858342418, https://openalex.org/W3207999419, https://openalex.org/W4365211553, https://openalex.org/W2995310170, https://openalex.org/W4382239131, https://openalex.org/W6861991748, https://openalex.org/W6850783765, https://openalex.org/W3217587896, https://openalex.org/W6856926427, https://openalex.org/W6850493425, https://openalex.org/W4402670000, https://openalex.org/W6778883912, https://openalex.org/W2930586974, https://openalex.org/W3122315731, https://openalex.org/W6799025473, https://openalex.org/W4391642957, https://openalex.org/W4387092445, https://openalex.org/W6848443141, https://openalex.org/W2889928394, https://openalex.org/W3157506437, https://openalex.org/W4292779060, https://openalex.org/W4391943462, https://openalex.org/W3212867051, https://openalex.org/W3212890323, https://openalex.org/W4390305765, https://openalex.org/W4388651085, https://openalex.org/W4387634898, https://openalex.org/W4382239356, https://openalex.org/W4315588609, https://openalex.org/W3097294131, https://openalex.org/W4360888751, https://openalex.org/W4321854470, https://openalex.org/W3171665133, https://openalex.org/W2524046867, https://openalex.org/W4306884390 |
| referenced_works_count | 90 |
| abstract_inverted_index.a | 2, 190, 260 |
| abstract_inverted_index.AI | 108 |
| abstract_inverted_index.In | 161 |
| abstract_inverted_index.We | 174, 210 |
| abstract_inverted_index.an | 113 |
| abstract_inverted_index.as | 36, 102 |
| abstract_inverted_index.by | 176 |
| abstract_inverted_index.in | 14, 27, 54, 57, 68, 79, 92, 116, 138, 169, 182, 235, 254, 265 |
| abstract_inverted_index.is | 246 |
| abstract_inverted_index.of | 20, 24, 30, 104, 135, 185, 196, 238 |
| abstract_inverted_index.to | 45, 130, 252 |
| abstract_inverted_index.we | 164, 221 |
| abstract_inverted_index.TSF | 98, 186, 253 |
| abstract_inverted_index.The | 17 |
| abstract_inverted_index.and | 42, 56, 83, 89, 155, 187, 218, 230, 257, 268 |
| abstract_inverted_index.are | 51, 159, 271 |
| abstract_inverted_index.for | 5, 86, 110, 172, 263 |
| abstract_inverted_index.has | 10, 38 |
| abstract_inverted_index.new | 191 |
| abstract_inverted_index.one | 103 |
| abstract_inverted_index.our | 58 |
| abstract_inverted_index.out | 101, 223 |
| abstract_inverted_index.the | 21, 28, 93, 105, 133, 139, 166, 178, 183, 194, 206, 227, 236, 247 |
| abstract_inverted_index.who | 270 |
| abstract_inverted_index.Deep | 0, 96 |
| abstract_inverted_index.TSF. | 173, 243 |
| abstract_inverted_index.This | 244 |
| abstract_inverted_index.age, | 141 |
| abstract_inverted_index.also | 211 |
| abstract_inverted_index.been | 11 |
| abstract_inverted_index.data | 137 |
| abstract_inverted_index.deal | 131 |
| abstract_inverted_index.deep | 25, 122, 170, 197, 239 |
| abstract_inverted_index.etc. | 151 |
| abstract_inverted_index.face | 126 |
| abstract_inverted_index.five | 208 |
| abstract_inverted_index.from | 193 |
| abstract_inverted_index.high | 148 |
| abstract_inverted_index.just | 272 |
| abstract_inverted_index.many | 15, 62 |
| abstract_inverted_index.more | 156 |
| abstract_inverted_index.most | 106, 248 |
| abstract_inverted_index.need | 129 |
| abstract_inverted_index.out. | 274 |
| abstract_inverted_index.over | 205 |
| abstract_inverted_index.past | 207 |
| abstract_inverted_index.risk | 77 |
| abstract_inverted_index.role | 115 |
| abstract_inverted_index.such | 35 |
| abstract_inverted_index.then | 188 |
| abstract_inverted_index.they | 128 |
| abstract_inverted_index.this | 162, 266 |
| abstract_inverted_index.time | 31 |
| abstract_inverted_index.used | 214 |
| abstract_inverted_index.will | 258 |
| abstract_inverted_index.with | 132, 226, 242 |
| abstract_inverted_index.(AI), | 9 |
| abstract_inverted_index.These | 49 |
| abstract_inverted_index.areas | 63 |
| abstract_inverted_index.begin | 175 |
| abstract_inverted_index.daily | 59 |
| abstract_inverted_index.field | 29, 184, 237, 267 |
| abstract_inverted_index.flow, | 74 |
| abstract_inverted_index.index | 262 |
| abstract_inverted_index.novel | 153 |
| abstract_inverted_index.paper | 245 |
| abstract_inverted_index.point | 222 |
| abstract_inverted_index.power | 69 |
| abstract_inverted_index.price | 84 |
| abstract_inverted_index.sales | 81 |
| abstract_inverted_index.shown | 39 |
| abstract_inverted_index.stand | 100 |
| abstract_inverted_index.still | 125 |
| abstract_inverted_index.tasks | 99 |
| abstract_inverted_index.those | 269 |
| abstract_inverted_index.years | 256 |
| abstract_inverted_index.(TSF), | 34 |
| abstract_inverted_index.field. | 95 |
| abstract_inverted_index.future | 233 |
| abstract_inverted_index.issues | 225 |
| abstract_inverted_index.latest | 22, 167 |
| abstract_inverted_index.lives, | 60 |
| abstract_inverted_index.longer | 143 |
| abstract_inverted_index.models | 124 |
| abstract_inverted_index.neural | 198 |
| abstract_inverted_index.paper, | 163 |
| abstract_inverted_index.recent | 179, 255 |
| abstract_inverted_index.reduce | 146 |
| abstract_inverted_index.review | 165, 250 |
| abstract_inverted_index.series | 32 |
| abstract_inverted_index.trends | 181 |
| abstract_inverted_index.widely | 52 |
| abstract_inverted_index.years. | 209 |
| abstract_inverted_index.achieve | 142 |
| abstract_inverted_index.applied | 13 |
| abstract_inverted_index.complex | 118 |
| abstract_inverted_index.control | 78 |
| abstract_inverted_index.crucial | 3 |
| abstract_inverted_index.current | 224 |
| abstract_inverted_index.fields. | 16 |
| abstract_inverted_index.gradual | 18 |
| abstract_inverted_index.medical | 94 |
| abstract_inverted_index.methods | 154 |
| abstract_inverted_index.metrics | 217 |
| abstract_inverted_index.models, | 200 |
| abstract_inverted_index.network | 199 |
| abstract_inverted_index.playing | 112 |
| abstract_inverted_index.present | 53 |
| abstract_inverted_index.propose | 189 |
| abstract_inverted_index.provide | 259 |
| abstract_inverted_index.ranges, | 145 |
| abstract_inverted_index.related | 251 |
| abstract_inverted_index.results | 43 |
| abstract_inverted_index.suggest | 231 |
| abstract_inverted_index.traffic | 73 |
| abstract_inverted_index.Finally, | 220 |
| abstract_inverted_index.However, | 121 |
| abstract_inverted_index.academia | 55 |
| abstract_inverted_index.articles | 203 |
| abstract_inverted_index.combined | 241 |
| abstract_inverted_index.commonly | 213 |
| abstract_inverted_index.compared | 44 |
| abstract_inverted_index.covering | 61, 202 |
| abstract_inverted_index.detailed | 261 |
| abstract_inverted_index.existing | 228 |
| abstract_inverted_index.finance, | 80 |
| abstract_inverted_index.learning | 26, 123, 171, 240 |
| abstract_inverted_index.methods. | 48 |
| abstract_inverted_index.organize | 212 |
| abstract_inverted_index.pandemic | 90 |
| abstract_inverted_index.starting | 273 |
| abstract_inverted_index.systems, | 70 |
| abstract_inverted_index.taxonomy | 192 |
| abstract_inverted_index.trading, | 76 |
| abstract_inverted_index.valuable | 107 |
| abstract_inverted_index.achieving | 6 |
| abstract_inverted_index.challenge | 134 |
| abstract_inverted_index.datasets. | 219 |
| abstract_inverted_index.effective | 157 |
| abstract_inverted_index.excellent | 40 |
| abstract_inverted_index.important | 114 |
| abstract_inverted_index.including | 64 |
| abstract_inverted_index.learning, | 1 |
| abstract_inverted_index.promising | 232 |
| abstract_inverted_index.published | 204 |
| abstract_inverted_index.rainfall, | 72 |
| abstract_inverted_index.research, | 111 |
| abstract_inverted_index.scenarios | 109 |
| abstract_inverted_index.solutions | 158, 229 |
| abstract_inverted_index.technique | 4 |
| abstract_inverted_index.Therefore, | 152 |
| abstract_inverted_index.artificial | 7 |
| abstract_inverted_index.commercial | 87 |
| abstract_inverted_index.companies, | 88 |
| abstract_inverted_index.directions | 234 |
| abstract_inverted_index.essential. | 160 |
| abstract_inverted_index.evaluation | 216 |
| abstract_inverted_index.explaining | 117 |
| abstract_inverted_index.operations | 82 |
| abstract_inverted_index.phenomena. | 120 |
| abstract_inverted_index.prediction | 91 |
| abstract_inverted_index.real-world | 119 |
| abstract_inverted_index.application | 19 |
| abstract_inverted_index.challenges: | 127 |
| abstract_inverted_index.complexity, | 150 |
| abstract_inverted_index.consumption | 67 |
| abstract_inverted_index.development | 180 |
| abstract_inverted_index.electricity | 66 |
| abstract_inverted_index.excessively | 147 |
| abstract_inverted_index.forecasting | 33, 65, 144 |
| abstract_inverted_index.information | 140 |
| abstract_inverted_index.introducing | 177 |
| abstract_inverted_index.large-scale | 136 |
| abstract_inverted_index.performance | 41 |
| abstract_inverted_index.perspective | 195 |
| abstract_inverted_index.predictions | 85 |
| abstract_inverted_index.researchers | 264 |
| abstract_inverted_index.statistical | 47 |
| abstract_inverted_index.traditional | 46 |
| abstract_inverted_index.applications | 50 |
| abstract_inverted_index.developments | 168 |
| abstract_inverted_index.experimental | 215 |
| abstract_inverted_index.intelligence | 8 |
| abstract_inverted_index.quantitative | 75 |
| abstract_inverted_index.successfully | 12 |
| abstract_inverted_index.Transformers, | 37 |
| abstract_inverted_index.architectures | 23 |
| abstract_inverted_index.comprehensive | 249 |
| abstract_inverted_index.computational | 149 |
| abstract_inverted_index.learning-based | 97 |
| abstract_inverted_index.meteorological | 71 |
| abstract_inverted_index.comprehensively | 201 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| corresponding_author_ids | https://openalex.org/A5017052768 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I111950717 |
| citation_normalized_percentile.value | 0.99879585 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |