Modelling multiple seasonalities with ARIMA: Forecasting Madrid NO2 hourly pollution levels Article Swipe
Matias Luis Avila
,
Andrés M. Alonso
,
Daniel Peña
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2860239/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2860239/v1
Multiple seasonalities often appear in high-frequency data. In this context multiple seasonal components are usually modelled in a deterministic way by trigonometric functions or dummy variables. This assumption may be too strict. Instead, a more flexible model is to allow the seasonality to slowly change as a seasonal Autoregressive Integrated Moving Average model, where the seasonality is modelled as a stochastic processes. In this study, we propose to model them iteratively, combining different seasonal Autoregressive Integrated Moving Average models. To this end, we test the proposed methodology with Madrid's NO 2 hourly measurements of pollutants with daily, weekly and annual seasonalities, due to human activity and weather conditions. Here, we demonstrate the usefulness of our approach by comparing it with other methodological approaches proposed for this type of data. In an extensive exercise involving 15-year hourly forecasts, we show that the proposed procedure performs very well in predicting hourly pollution over a 24-h horizon and improves on alternative procedures. Additionally, the impact on the predictions of covariates such as wind speed, temperature and festivities were evaluated.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2860239/v1
- https://www.researchsquare.com/article/rs-2860239/latest.pdf
- OA Status
- green
- Cited By
- 2
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4367312393
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4367312393Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-2860239/v1Digital Object Identifier
- Title
-
Modelling multiple seasonalities with ARIMA: Forecasting Madrid NO2 hourly pollution levelsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-28Full publication date if available
- Authors
-
Matias Luis Avila, Andrés M. Alonso, Daniel PeñaList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-2860239/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-2860239/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-2860239/latest.pdfDirect OA link when available
- Concepts
-
Seasonality, Autoregressive integrated moving average, Context (archaeology), Econometrics, Meteorology, Environmental science, Statistics, Mathematics, Climatology, Geography, Time series, Geology, ArchaeologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4367312393 |
|---|---|
| doi | https://doi.org/10.21203/rs.3.rs-2860239/v1 |
| ids.doi | https://doi.org/10.21203/rs.3.rs-2860239/v1 |
| ids.openalex | https://openalex.org/W4367312393 |
| fwci | 0.46728563 |
| type | preprint |
| title | Modelling multiple seasonalities with ARIMA: Forecasting Madrid NO2 hourly pollution levels |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10190 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9976999759674072 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2307 |
| topics[0].subfield.display_name | Health, Toxicology and Mutagenesis |
| topics[0].display_name | Air Quality and Health Impacts |
| topics[1].id | https://openalex.org/T12120 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9965999722480774 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2305 |
| topics[1].subfield.display_name | Environmental Engineering |
| topics[1].display_name | Air Quality Monitoring and Forecasting |
| topics[2].id | https://openalex.org/T12095 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9937000274658203 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2203 |
| topics[2].subfield.display_name | Automotive Engineering |
| topics[2].display_name | Vehicle emissions and performance |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C125403950 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7488752603530884 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q2111082 |
| concepts[0].display_name | Seasonality |
| concepts[1].id | https://openalex.org/C24338571 |
| concepts[1].level | 3 |
| concepts[1].score | 0.6814243793487549 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q2566298 |
| concepts[1].display_name | Autoregressive integrated moving average |
| concepts[2].id | https://openalex.org/C2779343474 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6240440607070923 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q3109175 |
| concepts[2].display_name | Context (archaeology) |
| concepts[3].id | https://openalex.org/C149782125 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5165337920188904 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[3].display_name | Econometrics |
| concepts[4].id | https://openalex.org/C153294291 |
| concepts[4].level | 1 |
| concepts[4].score | 0.47007280588150024 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q25261 |
| concepts[4].display_name | Meteorology |
| concepts[5].id | https://openalex.org/C39432304 |
| concepts[5].level | 0 |
| concepts[5].score | 0.42457252740859985 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[5].display_name | Environmental science |
| concepts[6].id | https://openalex.org/C105795698 |
| concepts[6].level | 1 |
| concepts[6].score | 0.4242086112499237 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[6].display_name | Statistics |
| concepts[7].id | https://openalex.org/C33923547 |
| concepts[7].level | 0 |
| concepts[7].score | 0.34906822443008423 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[7].display_name | Mathematics |
| concepts[8].id | https://openalex.org/C49204034 |
| concepts[8].level | 1 |
| concepts[8].score | 0.34052780270576477 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q52139 |
| concepts[8].display_name | Climatology |
| concepts[9].id | https://openalex.org/C205649164 |
| concepts[9].level | 0 |
| concepts[9].score | 0.27268776297569275 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[9].display_name | Geography |
| concepts[10].id | https://openalex.org/C151406439 |
| concepts[10].level | 2 |
| concepts[10].score | 0.2210507094860077 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q186588 |
| concepts[10].display_name | Time series |
| concepts[11].id | https://openalex.org/C127313418 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[11].display_name | Geology |
| concepts[12].id | https://openalex.org/C166957645 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q23498 |
| concepts[12].display_name | Archaeology |
| keywords[0].id | https://openalex.org/keywords/seasonality |
| keywords[0].score | 0.7488752603530884 |
| keywords[0].display_name | Seasonality |
| keywords[1].id | https://openalex.org/keywords/autoregressive-integrated-moving-average |
| keywords[1].score | 0.6814243793487549 |
| keywords[1].display_name | Autoregressive integrated moving average |
| keywords[2].id | https://openalex.org/keywords/context |
| keywords[2].score | 0.6240440607070923 |
| keywords[2].display_name | Context (archaeology) |
| keywords[3].id | https://openalex.org/keywords/econometrics |
| keywords[3].score | 0.5165337920188904 |
| keywords[3].display_name | Econometrics |
| keywords[4].id | https://openalex.org/keywords/meteorology |
| keywords[4].score | 0.47007280588150024 |
| keywords[4].display_name | Meteorology |
| keywords[5].id | https://openalex.org/keywords/environmental-science |
| keywords[5].score | 0.42457252740859985 |
| keywords[5].display_name | Environmental science |
| keywords[6].id | https://openalex.org/keywords/statistics |
| keywords[6].score | 0.4242086112499237 |
| keywords[6].display_name | Statistics |
| keywords[7].id | https://openalex.org/keywords/mathematics |
| keywords[7].score | 0.34906822443008423 |
| keywords[7].display_name | Mathematics |
| keywords[8].id | https://openalex.org/keywords/climatology |
| keywords[8].score | 0.34052780270576477 |
| keywords[8].display_name | Climatology |
| keywords[9].id | https://openalex.org/keywords/geography |
| keywords[9].score | 0.27268776297569275 |
| keywords[9].display_name | Geography |
| keywords[10].id | https://openalex.org/keywords/time-series |
| keywords[10].score | 0.2210507094860077 |
| keywords[10].display_name | Time series |
| language | en |
| locations[0].id | doi:10.21203/rs.3.rs-2860239/v1 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306402450 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Research Square (Research Square) |
| locations[0].source.host_organization | https://openalex.org/I4210096694 |
| locations[0].source.host_organization_name | Research Square (United States) |
| locations[0].source.host_organization_lineage | https://openalex.org/I4210096694 |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.researchsquare.com/article/rs-2860239/latest.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.21203/rs.3.rs-2860239/v1 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5064476185 |
| authorships[0].author.orcid | https://orcid.org/0009-0003-2665-2242 |
| authorships[0].author.display_name | Matias Luis Avila |
| authorships[0].countries | ES |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I50357001 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Statistics, University Carlos III of Madrid, Madrid, Spain. |
| authorships[0].institutions[0].id | https://openalex.org/I50357001 |
| authorships[0].institutions[0].ror | https://ror.org/03ths8210 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I50357001 |
| authorships[0].institutions[0].country_code | ES |
| authorships[0].institutions[0].display_name | Universidad Carlos III de Madrid |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Matias Luis Avila |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Statistics, University Carlos III of Madrid, Madrid, Spain. |
| authorships[1].author.id | https://openalex.org/A5087607394 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-6112-2867 |
| authorships[1].author.display_name | Andrés M. Alonso |
| authorships[1].countries | ES |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I50357001 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Statistics and Institute Flores de Lemus, University Carlos III of Madrid, Madrid, Spain. |
| authorships[1].institutions[0].id | https://openalex.org/I50357001 |
| authorships[1].institutions[0].ror | https://ror.org/03ths8210 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I50357001 |
| authorships[1].institutions[0].country_code | ES |
| authorships[1].institutions[0].display_name | Universidad Carlos III de Madrid |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Andres M. Alonso |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Statistics and Institute Flores de Lemus, University Carlos III of Madrid, Madrid, Spain. |
| authorships[2].author.id | https://openalex.org/A5088521043 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-9137-1557 |
| authorships[2].author.display_name | Daniel Peña |
| authorships[2].countries | ES |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I50357001 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Statistics, University Carlos III of Madrid, Madrid, Spain. |
| authorships[2].institutions[0].id | https://openalex.org/I50357001 |
| authorships[2].institutions[0].ror | https://ror.org/03ths8210 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I50357001 |
| authorships[2].institutions[0].country_code | ES |
| authorships[2].institutions[0].display_name | Universidad Carlos III de Madrid |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Daniel Peña |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Statistics, University Carlos III of Madrid, Madrid, Spain. |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.researchsquare.com/article/rs-2860239/latest.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Modelling multiple seasonalities with ARIMA: Forecasting Madrid NO2 hourly pollution levels |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10190 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9976999759674072 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2307 |
| primary_topic.subfield.display_name | Health, Toxicology and Mutagenesis |
| primary_topic.display_name | Air Quality and Health Impacts |
| related_works | https://openalex.org/W3152330453, https://openalex.org/W1492179261, https://openalex.org/W1606171414, https://openalex.org/W3121595392, https://openalex.org/W1441464215, https://openalex.org/W2385822111, https://openalex.org/W602745269, https://openalex.org/W3124949403, https://openalex.org/W2479754577, https://openalex.org/W2552050053 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.21203/rs.3.rs-2860239/v1 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306402450 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| 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 | Research Square (Research Square) |
| best_oa_location.source.host_organization | https://openalex.org/I4210096694 |
| best_oa_location.source.host_organization_name | Research Square (United States) |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I4210096694 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.researchsquare.com/article/rs-2860239/latest.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.21203/rs.3.rs-2860239/v1 |
| primary_location.id | doi:10.21203/rs.3.rs-2860239/v1 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306402450 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Research Square (Research Square) |
| primary_location.source.host_organization | https://openalex.org/I4210096694 |
| primary_location.source.host_organization_name | Research Square (United States) |
| primary_location.source.host_organization_lineage | https://openalex.org/I4210096694 |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.researchsquare.com/article/rs-2860239/latest.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.21203/rs.3.rs-2860239/v1 |
| publication_date | 2023-04-28 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2064815513, https://openalex.org/W57910075, https://openalex.org/W1992282982, https://openalex.org/W3109735136, https://openalex.org/W2972862002, https://openalex.org/W2024760831, https://openalex.org/W2969748486, https://openalex.org/W3163934814, https://openalex.org/W3134159660, https://openalex.org/W1992866626, https://openalex.org/W2116512828, https://openalex.org/W2129488228, https://openalex.org/W2622241197, https://openalex.org/W2023419040, https://openalex.org/W2109316012, https://openalex.org/W3168673861, https://openalex.org/W2159227269, https://openalex.org/W2914576261, https://openalex.org/W3085119257, https://openalex.org/W1667277804, https://openalex.org/W2156636680, https://openalex.org/W2747599906, https://openalex.org/W2167036165, https://openalex.org/W2011227258, https://openalex.org/W1995351367, https://openalex.org/W212335114, https://openalex.org/W4229539396 |
| referenced_works_count | 27 |
| abstract_inverted_index.2 | 91 |
| abstract_inverted_index.a | 18, 34, 47, 60, 152 |
| abstract_inverted_index.In | 8, 63, 130 |
| abstract_inverted_index.NO | 90 |
| abstract_inverted_index.To | 80 |
| abstract_inverted_index.an | 131 |
| abstract_inverted_index.as | 46, 59, 169 |
| abstract_inverted_index.be | 30 |
| abstract_inverted_index.by | 21, 117 |
| abstract_inverted_index.in | 5, 17, 147 |
| abstract_inverted_index.is | 38, 57 |
| abstract_inverted_index.it | 119 |
| abstract_inverted_index.of | 94, 114, 128, 166 |
| abstract_inverted_index.on | 157, 163 |
| abstract_inverted_index.or | 24 |
| abstract_inverted_index.to | 39, 43, 68, 103 |
| abstract_inverted_index.we | 66, 83, 110, 138 |
| abstract_inverted_index.and | 99, 106, 155, 173 |
| abstract_inverted_index.are | 14 |
| abstract_inverted_index.due | 102 |
| abstract_inverted_index.for | 125 |
| abstract_inverted_index.may | 29 |
| abstract_inverted_index.our | 115 |
| abstract_inverted_index.the | 41, 55, 85, 112, 141, 161, 164 |
| abstract_inverted_index.too | 31 |
| abstract_inverted_index.way | 20 |
| abstract_inverted_index.24-h | 153 |
| abstract_inverted_index.This | 27 |
| abstract_inverted_index.end, | 82 |
| abstract_inverted_index.more | 35 |
| abstract_inverted_index.over | 151 |
| abstract_inverted_index.show | 139 |
| abstract_inverted_index.such | 168 |
| abstract_inverted_index.test | 84 |
| abstract_inverted_index.that | 140 |
| abstract_inverted_index.them | 70 |
| abstract_inverted_index.this | 9, 64, 81, 126 |
| abstract_inverted_index.type | 127 |
| abstract_inverted_index.very | 145 |
| abstract_inverted_index.well | 146 |
| abstract_inverted_index.were | 175 |
| abstract_inverted_index.wind | 170 |
| abstract_inverted_index.with | 88, 96, 120 |
| abstract_inverted_index.Here, | 109 |
| abstract_inverted_index.allow | 40 |
| abstract_inverted_index.data. | 7, 129 |
| abstract_inverted_index.dummy | 25 |
| abstract_inverted_index.human | 104 |
| abstract_inverted_index.model | 37, 69 |
| abstract_inverted_index.often | 3 |
| abstract_inverted_index.other | 121 |
| abstract_inverted_index.where | 54 |
| abstract_inverted_index.Moving | 51, 77 |
| abstract_inverted_index.annual | 100 |
| abstract_inverted_index.appear | 4 |
| abstract_inverted_index.change | 45 |
| abstract_inverted_index.daily, | 97 |
| abstract_inverted_index.hourly | 92, 136, 149 |
| abstract_inverted_index.impact | 162 |
| abstract_inverted_index.model, | 53 |
| abstract_inverted_index.slowly | 44 |
| abstract_inverted_index.speed, | 171 |
| abstract_inverted_index.study, | 65 |
| abstract_inverted_index.weekly | 98 |
| abstract_inverted_index.15-year | 135 |
| abstract_inverted_index.Average | 52, 78 |
| abstract_inverted_index.context | 10 |
| abstract_inverted_index.horizon | 154 |
| abstract_inverted_index.models. | 79 |
| abstract_inverted_index.propose | 67 |
| abstract_inverted_index.strict. | 32 |
| abstract_inverted_index.usually | 15 |
| abstract_inverted_index.weather | 107 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Instead, | 33 |
| abstract_inverted_index.Madrid's | 89 |
| abstract_inverted_index.Multiple | 1 |
| abstract_inverted_index.activity | 105 |
| abstract_inverted_index.approach | 116 |
| abstract_inverted_index.exercise | 133 |
| abstract_inverted_index.flexible | 36 |
| abstract_inverted_index.improves | 156 |
| abstract_inverted_index.modelled | 16, 58 |
| abstract_inverted_index.multiple | 11 |
| abstract_inverted_index.performs | 144 |
| abstract_inverted_index.proposed | 86, 124, 142 |
| abstract_inverted_index.seasonal | 12, 48, 74 |
| abstract_inverted_index.combining | 72 |
| abstract_inverted_index.comparing | 118 |
| abstract_inverted_index.different | 73 |
| abstract_inverted_index.extensive | 132 |
| abstract_inverted_index.functions | 23 |
| abstract_inverted_index.involving | 134 |
| abstract_inverted_index.pollution | 150 |
| abstract_inverted_index.procedure | 143 |
| abstract_inverted_index.Integrated | 50, 76 |
| abstract_inverted_index.approaches | 123 |
| abstract_inverted_index.assumption | 28 |
| abstract_inverted_index.components | 13 |
| abstract_inverted_index.covariates | 167 |
| abstract_inverted_index.evaluated. | 176 |
| abstract_inverted_index.forecasts, | 137 |
| abstract_inverted_index.pollutants | 95 |
| abstract_inverted_index.predicting | 148 |
| abstract_inverted_index.processes. | 62 |
| abstract_inverted_index.stochastic | 61 |
| abstract_inverted_index.usefulness | 113 |
| abstract_inverted_index.variables. | 26 |
| abstract_inverted_index.alternative | 158 |
| abstract_inverted_index.conditions. | 108 |
| abstract_inverted_index.demonstrate | 111 |
| abstract_inverted_index.festivities | 174 |
| abstract_inverted_index.methodology | 87 |
| abstract_inverted_index.predictions | 165 |
| abstract_inverted_index.procedures. | 159 |
| abstract_inverted_index.seasonality | 42, 56 |
| abstract_inverted_index.temperature | 172 |
| abstract_inverted_index.iteratively, | 71 |
| abstract_inverted_index.measurements | 93 |
| abstract_inverted_index.Additionally, | 160 |
| abstract_inverted_index.deterministic | 19 |
| abstract_inverted_index.seasonalities | 2 |
| abstract_inverted_index.trigonometric | 22 |
| abstract_inverted_index.Autoregressive | 49, 75 |
| abstract_inverted_index.high-frequency | 6 |
| abstract_inverted_index.methodological | 122 |
| abstract_inverted_index.seasonalities, | 101 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.57665277 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |