{"id":"https://openalex.org/W3207445952","doi":"https://doi.org/10.1109/igarss47720.2021.9554764","title":"Modified Deep Transformers for GNSS Time Series Prediction","display_name":"Modified Deep Transformers for GNSS Time Series Prediction","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3207445952","doi":"https://doi.org/10.1109/igarss47720.2021.9554764","mag":"3207445952"},"language":"en","primary_location":{"id":"doi:10.1109/igarss47720.2021.9554764","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554764","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5064926582","display_name":"Mostafa Kiani Shahvandi","orcid":"https://orcid.org/0000-0001-5705-7014"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]},{"id":"https://openalex.org/I4210095092","display_name":"Institute of Geodesy and Cartography","ror":"https://ror.org/00ph65r66","country_code":"PL","type":"facility","lineage":["https://openalex.org/I4210095092"]}],"countries":["CH","PL"],"is_corresponding":false,"raw_author_name":"Mostafa Kiani Shahvandi","raw_affiliation_strings":["Institute of Geodesy and Photogrammetry, ETH Zurich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Geodesy and Photogrammetry, ETH Zurich","institution_ids":["https://openalex.org/I4210095092","https://openalex.org/I35440088"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046082471","display_name":"Benedikt Soja","orcid":"https://orcid.org/0000-0002-7010-2147"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]},{"id":"https://openalex.org/I4210095092","display_name":"Institute of Geodesy and Cartography","ror":"https://ror.org/00ph65r66","country_code":"PL","type":"facility","lineage":["https://openalex.org/I4210095092"]}],"countries":["CH","PL"],"is_corresponding":false,"raw_author_name":"Benedikt Soja","raw_affiliation_strings":["Institute of Geodesy and Photogrammetry, ETH Zurich"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Geodesy and Photogrammetry, ETH Zurich","institution_ids":["https://openalex.org/I4210095092","https://openalex.org/I35440088"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.0074,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.87514226,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"8313","last_page":"8316"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13487","display_name":"Statistical and numerical algorithms","score":0.9915000200271606,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10655","display_name":"GNSS positioning and interference","score":0.9769999980926514,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gnss-applications","display_name":"GNSS applications","score":0.7465309500694275},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7403263449668884},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6805359721183777},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6114381551742554},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5844715237617493},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5788671970367432},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5785207152366638},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.47172585129737854},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44683200120925903},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4301125407218933},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4182501435279846},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36009055376052856},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.18362149596214294},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12226185202598572},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09158468246459961},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.0901263952255249},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.07887494564056396}],"concepts":[{"id":"https://openalex.org/C14279187","wikidata":"https://www.wikidata.org/wiki/Q5514012","display_name":"GNSS applications","level":3,"score":0.7465309500694275},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7403263449668884},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6805359721183777},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6114381551742554},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5844715237617493},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5788671970367432},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5785207152366638},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.47172585129737854},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44683200120925903},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4301125407218933},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4182501435279846},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36009055376052856},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.18362149596214294},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12226185202598572},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09158468246459961},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0901263952255249},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.07887494564056396},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss47720.2021.9554764","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554764","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1533861849","https://openalex.org/W1563281071","https://openalex.org/W1969456163","https://openalex.org/W2064675550","https://openalex.org/W2794778778","https://openalex.org/W2893726365","https://openalex.org/W2964121744","https://openalex.org/W3002709689","https://openalex.org/W4248313735","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6631943919","https://openalex.org/W6739901393","https://openalex.org/W6773071120"],"related_works":["https://openalex.org/W4386936491","https://openalex.org/W3110195467","https://openalex.org/W3033823235","https://openalex.org/W2881093137","https://openalex.org/W4386787199","https://openalex.org/W4384916587","https://openalex.org/W2163330330","https://openalex.org/W1982137911","https://openalex.org/W2606296569","https://openalex.org/W4288060108"],"abstract_inverted_index":{"Highly":[0],"accurate":[1],"time":[2,62],"series":[3,63],"prediction":[4,64,85],"in":[5,84],"the":[6,23,36,41,59,110],"field":[7],"of":[8,25,44,65],"geodesy":[9],"is":[10],"both":[11],"important":[12],"and":[13,40,47],"a":[14,28,51],"demanding":[15],"task.":[16],"For":[17],"this":[18,45,74],"problem":[19],"we":[20,88],"have":[21,33,49,57],"investigated":[22],"potentiality":[24],"deep":[26,29,52],"transformers,":[27],"learning":[30,53,95],"approach.":[31],"We":[32,56,71,101],"slightly":[34],"modified":[35],"original":[37],"network":[38],"architecture":[39],"optimization":[42],"procedure":[43],"model":[46],"thus":[48],"created":[50],"regression":[54],"framework.":[55],"applied":[58],"method":[60],"for":[61,109],"more":[66],"than":[67,78],"18000":[68],"GNSS":[69],"stations.":[70],"show":[72,89],"that":[73,90,103],"approach":[75],"performs":[76],"better":[77],"traditional":[79],"statistical":[80],"methods":[81],"by":[82,97],"21.5%":[83],"accuracy.":[86],"Furthermore,":[87],"it":[91],"outperforms":[92],"other":[93],"machine":[94],"algorithms":[96],"at":[98],"least":[99],"2.7%.":[100],"demonstrate":[102],"millimeter":[104],"accuracy":[105],"can":[106],"be":[107],"expected":[108],"prediction.":[111]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}