Regularization in Hierarchical Time Series Forecasting with Application to Electricity Smart Meter Data Article Swipe
Souhaib Ben Taieb
,
Jiafan Yu
,
Mateus Barreto
,
Ram Rajagopal
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v31i1.11167
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v31i1.11167
Accurate electricity demand forecast plays a key role in sustainable power systems. It enables better decision making in the planning of electricity generation and distribution for many use cases. The electricity demand data can often be represented in a hierarchical structure. For example, the electricity consumption of a whole country could be disaggregated by states, cities, and households. Hierarchical forecasts require not only good prediction accuracy at each level of the hierarchy, but also the consistency between different levels. State-of-the-art hierarchical forecasting methods usually apply adjustments on the individual level forecasts to satisfy the aggregation constraints. However, the high-dimensionality of the unpenalized regression problem and the estimation errors in the high-dimensional error covariance matrix can lead to increased variability in the revised forecasts with poor prediction performance. In order to provide more robustness to estimation errors in the adjustments, we present a new hierarchical forecasting algorithm that computes sparse adjustments while still preserving the aggregation constraints. We formulate the problem as a high-dimensional penalized regression, which can be efficiently solved using cyclical coordinate descent methods. We also conduct experiments using a large-scale hierarchical electricity demand data. The results confirm the effectiveness of our approach compared to state-of-the-art hierarchical forecasting methods, in both the sparsity of the adjustments and the prediction accuracy. The proposed approach to hierarchical forecasting could be useful for energy generation including solar and wind energy, as well as numerous other applications.
Related Topics
Concepts
Computer science
Curse of dimensionality
Electricity
Electricity price forecasting
Robustness (evolution)
Wind power
Coordinate descent
Mathematical optimization
Data mining
Econometrics
Artificial intelligence
Electricity market
Machine learning
Mathematics
Engineering
Biochemistry
Gene
Chemistry
Electrical engineering
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v31i1.11167
- https://ojs.aaai.org/index.php/AAAI/article/download/11167/11026
- OA Status
- diamond
- Cited By
- 26
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2604584762
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2604584762Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1609/aaai.v31i1.11167Digital Object Identifier
- Title
-
Regularization in Hierarchical Time Series Forecasting with Application to Electricity Smart Meter DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2017Year of publication
- Publication date
-
2017-02-12Full publication date if available
- Authors
-
Souhaib Ben Taieb, Jiafan Yu, Mateus Barreto, Ram RajagopalList of authors in order
- Landing page
-
https://doi.org/10.1609/aaai.v31i1.11167Publisher landing page
- PDF URL
-
https://ojs.aaai.org/index.php/AAAI/article/download/11167/11026Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://ojs.aaai.org/index.php/AAAI/article/download/11167/11026Direct OA link when available
- Concepts
-
Computer science, Curse of dimensionality, Electricity, Electricity price forecasting, Robustness (evolution), Wind power, Coordinate descent, Mathematical optimization, Data mining, Econometrics, Artificial intelligence, Electricity market, Machine learning, Mathematics, Engineering, Biochemistry, Gene, Chemistry, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
26Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 2, 2023: 1, 2022: 2, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
34Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2604584762 |
|---|---|
| doi | https://doi.org/10.1609/aaai.v31i1.11167 |
| ids.doi | https://doi.org/10.1609/aaai.v31i1.11167 |
| ids.mag | 2604584762 |
| ids.openalex | https://openalex.org/W2604584762 |
| fwci | 8.46554119 |
| type | article |
| title | Regularization in Hierarchical Time Series Forecasting with Application to Electricity Smart Meter Data |
| biblio.issue | 1 |
| biblio.volume | 31 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11052 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9993000030517578 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2208 |
| topics[0].subfield.display_name | Electrical and Electronic Engineering |
| topics[0].display_name | Energy Load and Power Forecasting |
| topics[1].id | https://openalex.org/T11918 |
| topics[1].field.id | https://openalex.org/fields/18 |
| topics[1].field.display_name | Decision Sciences |
| topics[1].score | 0.9983999729156494 |
| 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 | Forecasting Techniques and Applications |
| topics[2].id | https://openalex.org/T12368 |
| topics[2].field.id | https://openalex.org/fields/18 |
| topics[2].field.display_name | Decision Sciences |
| topics[2].score | 0.9912999868392944 |
| 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 | Grey System Theory Applications |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.6619894504547119 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C111030470 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5631206035614014 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1430460 |
| concepts[1].display_name | Curse of dimensionality |
| concepts[2].id | https://openalex.org/C206658404 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5207692980766296 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q12725 |
| concepts[2].display_name | Electricity |
| concepts[3].id | https://openalex.org/C2781104810 |
| concepts[3].level | 4 |
| concepts[3].score | 0.48895585536956787 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q23580049 |
| concepts[3].display_name | Electricity price forecasting |
| concepts[4].id | https://openalex.org/C63479239 |
| concepts[4].level | 3 |
| concepts[4].score | 0.4376242160797119 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q7353546 |
| concepts[4].display_name | Robustness (evolution) |
| concepts[5].id | https://openalex.org/C78600449 |
| concepts[5].level | 2 |
| concepts[5].score | 0.43753716349601746 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q43302 |
| concepts[5].display_name | Wind power |
| concepts[6].id | https://openalex.org/C157553263 |
| concepts[6].level | 2 |
| concepts[6].score | 0.43359941244125366 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q5168004 |
| concepts[6].display_name | Coordinate descent |
| concepts[7].id | https://openalex.org/C126255220 |
| concepts[7].level | 1 |
| concepts[7].score | 0.3978956937789917 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q141495 |
| concepts[7].display_name | Mathematical optimization |
| concepts[8].id | https://openalex.org/C124101348 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3829406797885895 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[8].display_name | Data mining |
| concepts[9].id | https://openalex.org/C149782125 |
| concepts[9].level | 1 |
| concepts[9].score | 0.35294127464294434 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[9].display_name | Econometrics |
| concepts[10].id | https://openalex.org/C154945302 |
| concepts[10].level | 1 |
| concepts[10].score | 0.2778034806251526 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[10].display_name | Artificial intelligence |
| concepts[11].id | https://openalex.org/C146733006 |
| concepts[11].level | 3 |
| concepts[11].score | 0.26998037099838257 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q676081 |
| concepts[11].display_name | Electricity market |
| concepts[12].id | https://openalex.org/C119857082 |
| concepts[12].level | 1 |
| concepts[12].score | 0.266804039478302 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[12].display_name | Machine learning |
| concepts[13].id | https://openalex.org/C33923547 |
| concepts[13].level | 0 |
| concepts[13].score | 0.12885233759880066 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[13].display_name | Mathematics |
| concepts[14].id | https://openalex.org/C127413603 |
| concepts[14].level | 0 |
| concepts[14].score | 0.12536224722862244 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[14].display_name | Engineering |
| concepts[15].id | https://openalex.org/C55493867 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[15].display_name | Biochemistry |
| concepts[16].id | https://openalex.org/C104317684 |
| concepts[16].level | 2 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[16].display_name | Gene |
| concepts[17].id | https://openalex.org/C185592680 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[17].display_name | Chemistry |
| concepts[18].id | https://openalex.org/C119599485 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q43035 |
| concepts[18].display_name | Electrical engineering |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.6619894504547119 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/curse-of-dimensionality |
| keywords[1].score | 0.5631206035614014 |
| keywords[1].display_name | Curse of dimensionality |
| keywords[2].id | https://openalex.org/keywords/electricity |
| keywords[2].score | 0.5207692980766296 |
| keywords[2].display_name | Electricity |
| keywords[3].id | https://openalex.org/keywords/electricity-price-forecasting |
| keywords[3].score | 0.48895585536956787 |
| keywords[3].display_name | Electricity price forecasting |
| keywords[4].id | https://openalex.org/keywords/robustness |
| keywords[4].score | 0.4376242160797119 |
| keywords[4].display_name | Robustness (evolution) |
| keywords[5].id | https://openalex.org/keywords/wind-power |
| keywords[5].score | 0.43753716349601746 |
| keywords[5].display_name | Wind power |
| keywords[6].id | https://openalex.org/keywords/coordinate-descent |
| keywords[6].score | 0.43359941244125366 |
| keywords[6].display_name | Coordinate descent |
| keywords[7].id | https://openalex.org/keywords/mathematical-optimization |
| keywords[7].score | 0.3978956937789917 |
| keywords[7].display_name | Mathematical optimization |
| keywords[8].id | https://openalex.org/keywords/data-mining |
| keywords[8].score | 0.3829406797885895 |
| keywords[8].display_name | Data mining |
| keywords[9].id | https://openalex.org/keywords/econometrics |
| keywords[9].score | 0.35294127464294434 |
| keywords[9].display_name | Econometrics |
| keywords[10].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[10].score | 0.2778034806251526 |
| keywords[10].display_name | Artificial intelligence |
| keywords[11].id | https://openalex.org/keywords/electricity-market |
| keywords[11].score | 0.26998037099838257 |
| keywords[11].display_name | Electricity market |
| keywords[12].id | https://openalex.org/keywords/machine-learning |
| keywords[12].score | 0.266804039478302 |
| keywords[12].display_name | Machine learning |
| keywords[13].id | https://openalex.org/keywords/mathematics |
| keywords[13].score | 0.12885233759880066 |
| keywords[13].display_name | Mathematics |
| keywords[14].id | https://openalex.org/keywords/engineering |
| keywords[14].score | 0.12536224722862244 |
| keywords[14].display_name | Engineering |
| language | en |
| locations[0].id | doi:10.1609/aaai.v31i1.11167 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210191458 |
| locations[0].source.issn | 2159-5399, 2374-3468 |
| locations[0].source.type | conference |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2159-5399 |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Proceedings of the AAAI Conference on Artificial Intelligence |
| locations[0].source.host_organization | https://openalex.org/P4310320058 |
| locations[0].source.host_organization_name | Association for the Advancement of Artificial Intelligence |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320058 |
| locations[0].source.host_organization_lineage_names | Association for the Advancement of Artificial Intelligence |
| locations[0].license | |
| locations[0].pdf_url | https://ojs.aaai.org/index.php/AAAI/article/download/11167/11026 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Proceedings of the AAAI Conference on Artificial Intelligence |
| locations[0].landing_page_url | https://doi.org/10.1609/aaai.v31i1.11167 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5036751620 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-4605-0834 |
| authorships[0].author.display_name | Souhaib Ben Taieb |
| authorships[0].countries | AU |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I2801239119, https://openalex.org/I56590836 |
| authorships[0].affiliations[0].raw_affiliation_string | Monash University |
| authorships[0].institutions[0].id | https://openalex.org/I2801239119 |
| authorships[0].institutions[0].ror | https://ror.org/02qa5kg76 |
| authorships[0].institutions[0].type | facility |
| authorships[0].institutions[0].lineage | https://openalex.org/I2801037857, https://openalex.org/I2801239119, https://openalex.org/I56590836 |
| authorships[0].institutions[0].country_code | AU |
| authorships[0].institutions[0].display_name | Australian Regenerative Medicine Institute |
| authorships[0].institutions[1].id | https://openalex.org/I56590836 |
| authorships[0].institutions[1].ror | https://ror.org/02bfwt286 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I56590836 |
| authorships[0].institutions[1].country_code | AU |
| authorships[0].institutions[1].display_name | Monash University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Souhaib Ben Taieb |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Monash University |
| authorships[1].author.id | https://openalex.org/A5077108199 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-7743-9431 |
| authorships[1].author.display_name | Jiafan Yu |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I97018004 |
| authorships[1].affiliations[0].raw_affiliation_string | Stanford University |
| authorships[1].institutions[0].id | https://openalex.org/I97018004 |
| authorships[1].institutions[0].ror | https://ror.org/00f54p054 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I97018004 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | Stanford University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Jiafan Yu |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Stanford University |
| authorships[2].author.id | https://openalex.org/A5109358999 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Mateus Barreto |
| authorships[2].countries | BR |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I181391015 |
| authorships[2].affiliations[0].raw_affiliation_string | Universidade Estadual de Campinas |
| authorships[2].institutions[0].id | https://openalex.org/I181391015 |
| authorships[2].institutions[0].ror | https://ror.org/04wffgt70 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I181391015 |
| authorships[2].institutions[0].country_code | BR |
| authorships[2].institutions[0].display_name | Universidade Estadual de Campinas (UNICAMP) |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Mateus Barreto |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Universidade Estadual de Campinas |
| authorships[3].author.id | https://openalex.org/A5028892842 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-4961-9539 |
| authorships[3].author.display_name | Ram Rajagopal |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I97018004 |
| authorships[3].affiliations[0].raw_affiliation_string | Stanford University |
| authorships[3].institutions[0].id | https://openalex.org/I97018004 |
| authorships[3].institutions[0].ror | https://ror.org/00f54p054 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I97018004 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | Stanford University |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Ram Rajagopal |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Stanford University |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://ojs.aaai.org/index.php/AAAI/article/download/11167/11026 |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Regularization in Hierarchical Time Series Forecasting with Application to Electricity Smart Meter Data |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11052 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9993000030517578 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2208 |
| primary_topic.subfield.display_name | Electrical and Electronic Engineering |
| primary_topic.display_name | Energy Load and Power Forecasting |
| related_works | https://openalex.org/W2117229703, https://openalex.org/W4296209631, https://openalex.org/W3120756218, https://openalex.org/W2395088808, https://openalex.org/W2139276415, https://openalex.org/W2187599770, https://openalex.org/W4389989272, https://openalex.org/W4297961870, https://openalex.org/W3003665221, https://openalex.org/W4281789920 |
| cited_by_count | 26 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 2 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 2 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 2 |
| counts_by_year[5].year | 2020 |
| counts_by_year[5].cited_by_count | 8 |
| counts_by_year[6].year | 2019 |
| counts_by_year[6].cited_by_count | 5 |
| counts_by_year[7].year | 2018 |
| counts_by_year[7].cited_by_count | 3 |
| counts_by_year[8].year | 2016 |
| counts_by_year[8].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1609/aaai.v31i1.11167 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210191458 |
| best_oa_location.source.issn | 2159-5399, 2374-3468 |
| best_oa_location.source.type | conference |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2159-5399 |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Proceedings of the AAAI Conference on Artificial Intelligence |
| best_oa_location.source.host_organization | https://openalex.org/P4310320058 |
| best_oa_location.source.host_organization_name | Association for the Advancement of Artificial Intelligence |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320058 |
| best_oa_location.source.host_organization_lineage_names | Association for the Advancement of Artificial Intelligence |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://ojs.aaai.org/index.php/AAAI/article/download/11167/11026 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Proceedings of the AAAI Conference on Artificial Intelligence |
| best_oa_location.landing_page_url | https://doi.org/10.1609/aaai.v31i1.11167 |
| primary_location.id | doi:10.1609/aaai.v31i1.11167 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210191458 |
| primary_location.source.issn | 2159-5399, 2374-3468 |
| primary_location.source.type | conference |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2159-5399 |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Proceedings of the AAAI Conference on Artificial Intelligence |
| primary_location.source.host_organization | https://openalex.org/P4310320058 |
| primary_location.source.host_organization_name | Association for the Advancement of Artificial Intelligence |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320058 |
| primary_location.source.host_organization_lineage_names | Association for the Advancement of Artificial Intelligence |
| primary_location.license | |
| primary_location.pdf_url | https://ojs.aaai.org/index.php/AAAI/article/download/11167/11026 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Proceedings of the AAAI Conference on Artificial Intelligence |
| primary_location.landing_page_url | https://doi.org/10.1609/aaai.v31i1.11167 |
| publication_date | 2017-02-12 |
| publication_year | 2017 |
| referenced_works | https://openalex.org/W2098826311, https://openalex.org/W2098207764, https://openalex.org/W2066881545, https://openalex.org/W6664875265, https://openalex.org/W2097360283, https://openalex.org/W6987374284, https://openalex.org/W6603182422, https://openalex.org/W3122820950, https://openalex.org/W2073210389, https://openalex.org/W2096571934, https://openalex.org/W2109316012, https://openalex.org/W6681562632, https://openalex.org/W7073825148, https://openalex.org/W1990512452, https://openalex.org/W6643946926, https://openalex.org/W2119862467, https://openalex.org/W6682861331, https://openalex.org/W2299975911, https://openalex.org/W2460896869, https://openalex.org/W6684715720, https://openalex.org/W2122825543, https://openalex.org/W6655920544, https://openalex.org/W2145509823, https://openalex.org/W2564702731, https://openalex.org/W2259636642, https://openalex.org/W2116512828, https://openalex.org/W2056636001, https://openalex.org/W2020925091, https://openalex.org/W79427069, https://openalex.org/W4294541781, https://openalex.org/W2582743722, https://openalex.org/W2152204644, https://openalex.org/W2165970106, https://openalex.org/W1975404935 |
| referenced_works_count | 34 |
| abstract_inverted_index.a | 5, 38, 47, 141, 161, 180 |
| abstract_inverted_index.In | 127 |
| abstract_inverted_index.It | 12 |
| abstract_inverted_index.We | 156, 175 |
| abstract_inverted_index.as | 160, 228, 230 |
| abstract_inverted_index.at | 66 |
| abstract_inverted_index.be | 35, 51, 167, 218 |
| abstract_inverted_index.by | 53 |
| abstract_inverted_index.in | 8, 17, 37, 108, 119, 136, 200 |
| abstract_inverted_index.of | 20, 46, 69, 99, 191, 204 |
| abstract_inverted_index.on | 86 |
| abstract_inverted_index.to | 91, 116, 129, 133, 195, 214 |
| abstract_inverted_index.we | 139 |
| abstract_inverted_index.For | 41 |
| abstract_inverted_index.The | 29, 186, 211 |
| abstract_inverted_index.and | 23, 56, 104, 207, 225 |
| abstract_inverted_index.but | 72 |
| abstract_inverted_index.can | 33, 114, 166 |
| abstract_inverted_index.for | 25, 220 |
| abstract_inverted_index.key | 6 |
| abstract_inverted_index.new | 142 |
| abstract_inverted_index.not | 61 |
| abstract_inverted_index.our | 192 |
| abstract_inverted_index.the | 18, 43, 70, 74, 87, 93, 97, 100, 105, 109, 120, 137, 153, 158, 189, 202, 205, 208 |
| abstract_inverted_index.use | 27 |
| abstract_inverted_index.also | 73, 176 |
| abstract_inverted_index.both | 201 |
| abstract_inverted_index.data | 32 |
| abstract_inverted_index.each | 67 |
| abstract_inverted_index.good | 63 |
| abstract_inverted_index.lead | 115 |
| abstract_inverted_index.many | 26 |
| abstract_inverted_index.more | 131 |
| abstract_inverted_index.only | 62 |
| abstract_inverted_index.poor | 124 |
| abstract_inverted_index.role | 7 |
| abstract_inverted_index.that | 146 |
| abstract_inverted_index.well | 229 |
| abstract_inverted_index.wind | 226 |
| abstract_inverted_index.with | 123 |
| abstract_inverted_index.apply | 84 |
| abstract_inverted_index.could | 50, 217 |
| abstract_inverted_index.data. | 185 |
| abstract_inverted_index.error | 111 |
| abstract_inverted_index.level | 68, 89 |
| abstract_inverted_index.often | 34 |
| abstract_inverted_index.order | 128 |
| abstract_inverted_index.other | 232 |
| abstract_inverted_index.plays | 4 |
| abstract_inverted_index.power | 10 |
| abstract_inverted_index.solar | 224 |
| abstract_inverted_index.still | 151 |
| abstract_inverted_index.using | 170, 179 |
| abstract_inverted_index.which | 165 |
| abstract_inverted_index.while | 150 |
| abstract_inverted_index.whole | 48 |
| abstract_inverted_index.better | 14 |
| abstract_inverted_index.cases. | 28 |
| abstract_inverted_index.demand | 2, 31, 184 |
| abstract_inverted_index.energy | 221 |
| abstract_inverted_index.errors | 107, 135 |
| abstract_inverted_index.making | 16 |
| abstract_inverted_index.matrix | 113 |
| abstract_inverted_index.solved | 169 |
| abstract_inverted_index.sparse | 148 |
| abstract_inverted_index.useful | 219 |
| abstract_inverted_index.between | 76 |
| abstract_inverted_index.cities, | 55 |
| abstract_inverted_index.conduct | 177 |
| abstract_inverted_index.confirm | 188 |
| abstract_inverted_index.country | 49 |
| abstract_inverted_index.descent | 173 |
| abstract_inverted_index.enables | 13 |
| abstract_inverted_index.energy, | 227 |
| abstract_inverted_index.levels. | 78 |
| abstract_inverted_index.methods | 82 |
| abstract_inverted_index.present | 140 |
| abstract_inverted_index.problem | 103, 159 |
| abstract_inverted_index.provide | 130 |
| abstract_inverted_index.require | 60 |
| abstract_inverted_index.results | 187 |
| abstract_inverted_index.revised | 121 |
| abstract_inverted_index.satisfy | 92 |
| abstract_inverted_index.states, | 54 |
| abstract_inverted_index.usually | 83 |
| abstract_inverted_index.Accurate | 0 |
| abstract_inverted_index.However, | 96 |
| abstract_inverted_index.accuracy | 65 |
| abstract_inverted_index.approach | 193, 213 |
| abstract_inverted_index.compared | 194 |
| abstract_inverted_index.computes | 147 |
| abstract_inverted_index.cyclical | 171 |
| abstract_inverted_index.decision | 15 |
| abstract_inverted_index.example, | 42 |
| abstract_inverted_index.forecast | 3 |
| abstract_inverted_index.methods, | 199 |
| abstract_inverted_index.methods. | 174 |
| abstract_inverted_index.numerous | 231 |
| abstract_inverted_index.planning | 19 |
| abstract_inverted_index.proposed | 212 |
| abstract_inverted_index.sparsity | 203 |
| abstract_inverted_index.systems. | 11 |
| abstract_inverted_index.accuracy. | 210 |
| abstract_inverted_index.algorithm | 145 |
| abstract_inverted_index.different | 77 |
| abstract_inverted_index.forecasts | 59, 90, 122 |
| abstract_inverted_index.formulate | 157 |
| abstract_inverted_index.including | 223 |
| abstract_inverted_index.increased | 117 |
| abstract_inverted_index.penalized | 163 |
| abstract_inverted_index.coordinate | 172 |
| abstract_inverted_index.covariance | 112 |
| abstract_inverted_index.estimation | 106, 134 |
| abstract_inverted_index.generation | 22, 222 |
| abstract_inverted_index.hierarchy, | 71 |
| abstract_inverted_index.individual | 88 |
| abstract_inverted_index.prediction | 64, 125, 209 |
| abstract_inverted_index.preserving | 152 |
| abstract_inverted_index.regression | 102 |
| abstract_inverted_index.robustness | 132 |
| abstract_inverted_index.structure. | 40 |
| abstract_inverted_index.adjustments | 85, 149, 206 |
| abstract_inverted_index.aggregation | 94, 154 |
| abstract_inverted_index.consistency | 75 |
| abstract_inverted_index.consumption | 45 |
| abstract_inverted_index.efficiently | 168 |
| abstract_inverted_index.electricity | 1, 21, 30, 44, 183 |
| abstract_inverted_index.experiments | 178 |
| abstract_inverted_index.forecasting | 81, 144, 198, 216 |
| abstract_inverted_index.households. | 57 |
| abstract_inverted_index.large-scale | 181 |
| abstract_inverted_index.regression, | 164 |
| abstract_inverted_index.represented | 36 |
| abstract_inverted_index.sustainable | 9 |
| abstract_inverted_index.unpenalized | 101 |
| abstract_inverted_index.variability | 118 |
| abstract_inverted_index.Hierarchical | 58 |
| abstract_inverted_index.adjustments, | 138 |
| abstract_inverted_index.constraints. | 95, 155 |
| abstract_inverted_index.distribution | 24 |
| abstract_inverted_index.hierarchical | 39, 80, 143, 182, 197, 215 |
| abstract_inverted_index.performance. | 126 |
| abstract_inverted_index.applications. | 233 |
| abstract_inverted_index.disaggregated | 52 |
| abstract_inverted_index.effectiveness | 190 |
| abstract_inverted_index.State-of-the-art | 79 |
| abstract_inverted_index.high-dimensional | 110, 162 |
| abstract_inverted_index.state-of-the-art | 196 |
| abstract_inverted_index.high-dimensionality | 98 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.98860934 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |