Publication Information

{'id': 'https://openalex.org/W4287185323', 'doi': 'https://doi.org/10.48550/arxiv.2105.02027', 'title': 'Non-Autoregressive vs Autoregressive Neural Networks for System Identification', 'display_name': 'Non-Autoregressive vs Autoregressive Neural Networks for System Identification', 'publication_year': 2021, 'publication_date': '2021-01-01', 'ids': {'openalex': 'https://openalex.org/W4287185323', 'doi': 'https://doi.org/10.48550/arxiv.2105.02027'}, 'language': 'en', 'primary_location': {'is_oa': True, 'landing_page_url': 'https://arxiv.org/abs/2105.02027', 'pdf_url': None, 'source': {'id': 'https://openalex.org/S4306400194', 'display_name': 'arXiv (Cornell University)', 'issn_l': None, 'issn': None, 'is_oa': True, 'is_in_doaj': False, 'is_core': False, 'host_organization': 'https://openalex.org/I205783295', 'host_organization_name': 'Cornell University', 'host_organization_lineage': ['https://openalex.org/I205783295'], 'host_organization_lineage_names': ['Cornell University'], 'type': 'repository'}, 'license': 'other-oa', 'license_id': 'https://openalex.org/licenses/other-oa', 'version': 'submittedVersion', 'is_accepted': False, 'is_published': False}, 'type': 'preprint', 'type_crossref': 'posted-content', 'indexed_in': ['arxiv', 'datacite'], 'open_access': {'is_oa': True, 'oa_status': 'green', 'oa_url': 'https://arxiv.org/abs/2105.02027', 'any_repository_has_fulltext': True}, 'authorships': [{'author_position': 'first', 'author': {'id': 'https://openalex.org/A5084181299', 'display_name': 'Daniel Weber', 'orcid': 'https://orcid.org/0000-0001-6184-0570'}, 'institutions': [], 'countries': [], 'is_corresponding': False, 'raw_author_name': 'Weber, Daniel', 'raw_affiliation_strings': [], 'affiliations': []}, {'author_position': 'last', 'author': {'id': 'https://openalex.org/A5043061050', 'display_name': 'Clemens Gühmann', 'orcid': 'https://orcid.org/0000-0002-2865-0078'}, 'institutions': [], 'countries': [], 'is_corresponding': False, 'raw_author_name': 'Gühmann, Clemens', 'raw_affiliation_strings': [], 'affiliations': []}], 'institution_assertions': [], 'countries_distinct_count': 0, 'institutions_distinct_count': 0, 'corresponding_author_ids': [], 'corresponding_institution_ids': [], 'apc_list': None, 'apc_paid': None, 'fwci': None, 'has_fulltext': False, 'cited_by_count': 0, 'citation_normalized_percentile': {'value': 0.0, 'is_in_top_1_percent': False, 'is_in_top_10_percent': False}, 'cited_by_percentile_year': {'min': 0, 'max': 57}, 'biblio': {'volume': None, 'issue': None, 'first_page': None, 'last_page': None}, 'is_retracted': False, 'is_paratext': False, 'primary_topic': {'id': 'https://openalex.org/T10320', 'display_name': 'Neural Networks and Applications', 'score': 0.9941, 'subfield': {'id': 'https://openalex.org/subfields/1702', 'display_name': 'Artificial Intelligence'}, '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/T10320', 'display_name': 'Neural Networks and Applications', 'score': 0.9941, 'subfield': {'id': 'https://openalex.org/subfields/1702', 'display_name': 'Artificial Intelligence'}, '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/T11512', 'display_name': 'Anomaly Detection Techniques and Applications', 'score': 0.9893, 'subfield': {'id': 'https://openalex.org/subfields/1702', 'display_name': 'Artificial Intelligence'}, '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/T10688', 'display_name': 'Image and Signal Denoising Methods', 'score': 0.9884, 'subfield': {'id': 'https://openalex.org/subfields/1707', 'display_name': 'Computer Vision and Pattern Recognition'}, 'field': {'id': 'https://openalex.org/fields/17', 'display_name': 'Computer Science'}, 'domain': {'id': 'https://openalex.org/domains/3', 'display_name': 'Physical Sciences'}}], 'keywords': [{'id': 'https://openalex.org/keywords/setar', 'display_name': 'SETAR', 'score': 0.59171194}, {'id': 'https://openalex.org/keywords/identification', 'display_name': 'Identification', 'score': 0.5808065}], 'concepts': [{'id': 'https://openalex.org/C159877910', 'wikidata': 'https://www.wikidata.org/wiki/Q2202883', 'display_name': 'Autoregressive model', 'level': 2, 'score': 0.9640175}, {'id': 'https://openalex.org/C42536954', 'wikidata': 'https://www.wikidata.org/wiki/Q7049462', 'display_name': 'Nonlinear autoregressive exogenous model', 'level': 3, 'score': 0.921216}, {'id': 'https://openalex.org/C194657046', 'wikidata': 'https://www.wikidata.org/wiki/Q7394685', 'display_name': 'STAR model', 'level': 4, 'score': 0.8014717}, {'id': 'https://openalex.org/C41008148', 'wikidata': 'https://www.wikidata.org/wiki/Q21198', 'display_name': 'Computer science', 'level': 0, 'score': 0.62652194}, {'id': 'https://openalex.org/C30795276', 'wikidata': 'https://www.wikidata.org/wiki/Q7389877', 'display_name': 'SETAR', 'level': 5, 'score': 0.59171194}, {'id': 'https://openalex.org/C50644808', 'wikidata': 'https://www.wikidata.org/wiki/Q192776', 'display_name': 'Artificial neural network', 'level': 2, 'score': 0.581718}, {'id': 'https://openalex.org/C116834253', 'wikidata': 'https://www.wikidata.org/wiki/Q2039217', 'display_name': 'Identification (biology)', 'level': 2, 'score': 0.5808065}, {'id': 'https://openalex.org/C132459708', 'wikidata': 'https://www.wikidata.org/wiki/Q744069', 'display_name': 'Extrapolation', 'level': 2, 'score': 0.55716044}, {'id': 'https://openalex.org/C81363708', 'wikidata': 'https://www.wikidata.org/wiki/Q17084460', 'display_name': 'Convolutional neural network', 'level': 2, 'score': 0.47219217}, {'id': 'https://openalex.org/C119247159', 'wikidata': 'https://www.wikidata.org/wiki/Q1366192', 'display_name': 'System identification', 'level': 3, 'score': 0.4693243}, {'id': 'https://openalex.org/C154945302', 'wikidata': 'https://www.wikidata.org/wiki/Q11660', 'display_name': 'Artificial intelligence', 'level': 1, 'score': 0.4043726}, {'id': 'https://openalex.org/C119857082', 'wikidata': 'https://www.wikidata.org/wiki/Q2539', 'display_name': 'Machine learning', 'level': 1, 'score': 0.3133851}, {'id': 'https://openalex.org/C151406439', 'wikidata': 'https://www.wikidata.org/wiki/Q186588', 'display_name': 'Time series', 'level': 2, 'score': 0.27800348}, {'id': 'https://openalex.org/C24338571', 'wikidata': 'https://www.wikidata.org/wiki/Q2566298', 'display_name': 'Autoregressive integrated moving average', 'level': 3, 'score': 0.27504683}, {'id': 'https://openalex.org/C124101348', 'wikidata': 'https://www.wikidata.org/wiki/Q172491', 'display_name': 'Data mining', 'level': 1, 'score': 0.21301246}, {'id': 'https://openalex.org/C149782125', 'wikidata': 'https://www.wikidata.org/wiki/Q160039', 'display_name': 'Econometrics', 'level': 1, 'score': 0.19349313}, {'id': 'https://openalex.org/C105795698', 'wikidata': 'https://www.wikidata.org/wiki/Q12483', 'display_name': 'Statistics', 'level': 1, 'score': 0.14006758}, {'id': 'https://openalex.org/C33923547', 'wikidata': 'https://www.wikidata.org/wiki/Q395', 'display_name': 'Mathematics', 'level': 0, 'score': 0.13894394}, {'id': 'https://openalex.org/C59822182', 'wikidata': 'https://www.wikidata.org/wiki/Q441', 'display_name': 'Botany', '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}, {'id': 'https://openalex.org/C2780009758', 'wikidata': 'https://www.wikidata.org/wiki/Q6804172', 'display_name': 'Measure (data warehouse)', 'level': 2, 'score': 0.0}], 'mesh': [], 'locations_count': 3, 'locations': [{'is_oa': True, 'landing_page_url': 'https://arxiv.org/abs/2105.02027', 'pdf_url': None, 'source': {'id': 'https://openalex.org/S4306400194', 'display_name': 'arXiv (Cornell University)', 'issn_l': None, 'issn': None, 'is_oa': True, 'is_in_doaj': False, 'is_core': False, 'host_organization': 'https://openalex.org/I205783295', 'host_organization_name': 'Cornell University', 'host_organization_lineage': ['https://openalex.org/I205783295'], 'host_organization_lineage_names': ['Cornell University'], 'type': 'repository'}, 'license': 'other-oa', 'license_id': 'https://openalex.org/licenses/other-oa', 'version': 'submittedVersion', 'is_accepted': False, 'is_published': False}, {'is_oa': True, 'landing_page_url': 'http://arxiv.org/abs/2105.02027', 'pdf_url': 'http://arxiv.org/pdf/2105.02027', 'source': {'id': 'https://openalex.org/S4306400194', 'display_name': 'arXiv (Cornell University)', 'issn_l': None, 'issn': None, 'is_oa': True, 'is_in_doaj': False, 'is_core': False, 'host_organization': 'https://openalex.org/I205783295', 'host_organization_name': 'Cornell University', 'host_organization_lineage': ['https://openalex.org/I205783295'], 'host_organization_lineage_names': ['Cornell University'], 'type': 'repository'}, 'license': None, 'license_id': None, 'version': 'submittedVersion', 'is_accepted': False, 'is_published': False}, {'is_oa': False, 'landing_page_url': 'https://api.datacite.org/dois/10.48550/arxiv.2105.02027', 'pdf_url': None, 'source': {'id': 'https://openalex.org/S4393179698', 'display_name': 'DataCite API', 'issn_l': None, 'issn': None, 'is_oa': True, 'is_in_doaj': False, 'is_core': False, 'host_organization': 'https://openalex.org/I4210145204', 'host_organization_name': 'DataCite', 'host_organization_lineage': ['https://openalex.org/I4210145204'], 'host_organization_lineage_names': ['DataCite'], 'type': 'metadata'}, 'license': None, 'license_id': None, 'version': None}], 'best_oa_location': {'is_oa': True, 'landing_page_url': 'https://arxiv.org/abs/2105.02027', 'pdf_url': None, 'source': {'id': 'https://openalex.org/S4306400194', 'display_name': 'arXiv (Cornell University)', 'issn_l': None, 'issn': None, 'is_oa': True, 'is_in_doaj': False, 'is_core': False, 'host_organization': 'https://openalex.org/I205783295', 'host_organization_name': 'Cornell University', 'host_organization_lineage': ['https://openalex.org/I205783295'], 'host_organization_lineage_names': ['Cornell University'], 'type': 'repository'}, 'license': 'other-oa', 'license_id': 'https://openalex.org/licenses/other-oa', 'version': 'submittedVersion', 'is_accepted': False, 'is_published': False}, 'sustainable_development_goals': [{'display_name': 'Peace, justice, and strong institutions', 'score': 0.47, 'id': 'https://metadata.un.org/sdg/16'}], 'grants': [], 'datasets': [], 'versions': [], 'referenced_works_count': 0, 'referenced_works': [], 'related_works': ['https://openalex.org/W4320078083', 'https://openalex.org/W4287185323', 'https://openalex.org/W4238343629', 'https://openalex.org/W3123153965', 'https://openalex.org/W304218021', 'https://openalex.org/W2111126525', 'https://openalex.org/W2019155478', 'https://openalex.org/W1988789713', 'https://openalex.org/W1611117054', 'https://openalex.org/W1547559323'], 'abstract_inverted_index': {'The': [0], 'application': [1], 'of': [2, 18, 24, 28, 35, 74, 79, 88, 127], 'neural': [3, 53, 101, 135], 'networks': [4, 102], 'to': [5], 'non-linear': [6], 'dynamic': [7, 49], 'system': [8, 38, 92, 120, 137], 'identification': [9, 93, 121, 138], 'tasks': [10], 'has': [11], 'a': [12, 33, 37, 80], 'long': [13], 'history,': [14], 'which': [15, 43], 'consists': [16], 'mostly': [17], 'autoregressive': [19, 75, 113], 'approaches.': [20], 'Autoregression,': [21], 'the': [22, 25, 69, 85, 99, 128, 132, 142, 146], 'usage': [23], 'model': [26], 'outputs': [27], 'previous': [29], 'time': [30, 41], 'steps,': [31, 42], 'is': [32, 44, 131], 'method': [34], 'transferring': [36], 'state': [39], 'between': [40], 'not': [45], 'necessary': [46], 'for': [47], 'modeling': [48], 'systems': [50], 'with': [51, 116], 'modern': [52], 'network': [54], 'structures,': [55], 'such': [56], 'as': [57, 109, 111], 'gated': [58], 'recurrent': [59], 'units': [60], '(GRUs)': [61], 'and': [62, 71, 76, 82, 106, 140], 'Temporal': [63], 'Convolutional': [64], 'Networks': [65], '(TCNs).': [66], 'We': [67], 'compare': [68], 'accuracy': [70], 'execution': [72], 'performance': [73], 'non-autoregressive': [77, 100, 129], 'implementations': [78], 'GRU': [81, 130], 'TCN': [83], 'on': [84], 'simulation': [86], 'task': [87], 'three': [89], 'publicly': [90], 'available': [91], 'benchmarks.': [94], 'Our': [95], 'results': [96], 'show,': [97, 123], 'that': [98, 124], 'are': [103], 'significantly': [104], 'faster': [105], 'at': [107], 'least': [108], 'accurate': [110], 'their': [112], 'counterparts.': [114], 'Comparisons': [115], 'other': [117], 'state-of-the-art': [118], 'black-box': [119, 149], 'methods': [122], 'our': [125], 'implementation': [126], 'best': [133, 147], 'performing': [134, 148], 'network-based': [136], 'method,': [139], 'in': [141], 'benchmarks': [143], 'without': [144], 'extrapolation,': [145], 'method.': [150]}, 'cited_by_api_url': 'https://api.openalex.org/works?filter=cites:W4287185323', 'counts_by_year': [], 'updated_date': '2024-12-06T12:00:22.298825', 'created_date': '2022-07-25'}