{"id":"https://openalex.org/W2961697600","doi":"https://doi.org/10.23919/mva.2019.8757981","title":"A very concise feature representation for time series classification understanding","display_name":"A very concise feature representation for time series classification understanding","publication_year":2019,"publication_date":"2019-05-01","ids":{"openalex":"https://openalex.org/W2961697600","doi":"https://doi.org/10.23919/mva.2019.8757981","mag":"2961697600"},"language":"en","primary_location":{"id":"doi:10.23919/mva.2019.8757981","is_oa":false,"landing_page_url":"https://doi.org/10.23919/mva.2019.8757981","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 16th International Conference on Machine Vision Applications (MVA)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5088868498","display_name":"Pattreeya Tanisaro","orcid":null},"institutions":[{"id":"https://openalex.org/I170658231","display_name":"Osnabr\u00fcck University","ror":"https://ror.org/04qmmjx98","country_code":"DE","type":"education","lineage":["https://openalex.org/I170658231"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Pattreeya Tanisaro","raw_affiliation_strings":["University of Osnabr\u00fcck, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Osnabr\u00fcck, Germany","institution_ids":["https://openalex.org/I170658231"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036545001","display_name":"Gunther Heidemann","orcid":null},"institutions":[{"id":"https://openalex.org/I170658231","display_name":"Osnabr\u00fcck University","ror":"https://ror.org/04qmmjx98","country_code":"DE","type":"education","lineage":["https://openalex.org/I170658231"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Gunther Heidemann","raw_affiliation_strings":["University of Osnabr\u00fcck, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Osnabr\u00fcck, Germany","institution_ids":["https://openalex.org/I170658231"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170658231"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9995999932289124,"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":0.9995999932289124,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9962999820709229,"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/T10320","display_name":"Neural Networks and Applications","score":0.9927999973297119,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7812936305999756},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6517545580863953},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.629340648651123},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.6243771910667419},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.5437771081924438},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5237921476364136},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5096912384033203},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4939597249031067},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.48303017020225525},{"id":"https://openalex.org/keywords/hyperparameter","display_name":"Hyperparameter","score":0.47045719623565674},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.46825796365737915},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.45975232124328613},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.4476989209651947},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.43845197558403015},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4373641908168793},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.429192453622818},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.4212251305580139},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.39050012826919556}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7812936305999756},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6517545580863953},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.629340648651123},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.6243771910667419},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.5437771081924438},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5237921476364136},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5096912384033203},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4939597249031067},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.48303017020225525},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.47045719623565674},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46825796365737915},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.45975232124328613},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.4476989209651947},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.43845197558403015},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4373641908168793},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.429192453622818},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.4212251305580139},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.39050012826919556},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.23919/mva.2019.8757981","is_oa":false,"landing_page_url":"https://doi.org/10.23919/mva.2019.8757981","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 16th International Conference on Machine Vision Applications (MVA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6299999952316284,"display_name":"Life in Land","id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W789250018","https://openalex.org/W1950788856","https://openalex.org/W2025632878","https://openalex.org/W2099593264","https://openalex.org/W2290727245","https://openalex.org/W2296311849","https://openalex.org/W2307035320","https://openalex.org/W2585354796","https://openalex.org/W2626875243","https://openalex.org/W2766608677","https://openalex.org/W2783323081","https://openalex.org/W2786944751","https://openalex.org/W2898994947","https://openalex.org/W2952587893","https://openalex.org/W6640754710","https://openalex.org/W6698200468","https://openalex.org/W6755795618"],"related_works":["https://openalex.org/W1995622179","https://openalex.org/W4391160746","https://openalex.org/W1484111231","https://openalex.org/W1552543208","https://openalex.org/W2074396517","https://openalex.org/W2166963679","https://openalex.org/W2187269125","https://openalex.org/W1641615907","https://openalex.org/W3089231081","https://openalex.org/W2093956241"],"abstract_inverted_index":{"One":[0],"major":[1],"problem":[2],"of":[3,8,23,60,68,70,74,87,98,113,133,156,164,175],"time":[4,11,92,167],"series":[5,93,168],"analysis,":[6],"particularly":[7],"a":[9,82,122,126,142],"multivariate":[10,166],"series,":[12],"is":[13],"to":[14,31,43,89,107,124,141],"find":[15],"their":[16],"feature":[17,99,111],"representations.":[18,100],"Especially,":[19],"with":[20,35,185],"the":[21,33,45,54,65,71,75,91,105,110,114,131,138,154,161,186,189,195],"emerging":[22],"deep":[24,196],"recurrent":[25],"neural":[26],"networks":[27,34],"(RNNs),":[28],"researchers":[29],"opt":[30],"train":[32],"raw":[36],"signals":[37],"by":[38,136],"using":[39],"an":[40,118],"end-to-end":[41],"framework":[42],"achieve":[44],"highest":[46],"classification":[47,144,181],"accuracy.":[48],"Their":[49],"works":[50],"focus":[51],"on":[52],"modifying":[53],"network":[55],"models":[56],"and":[57,116,170,177,194],"fine-tuning":[58],"millions":[59],"hyperparameters;":[61],"however,":[62],"they":[63],"lack":[64],"required":[66],"level":[67],"understanding":[69],"intrinsic":[72],"properties":[73],"data.":[76],"In":[77],"our":[78,134],"work,":[79],"we":[80],"adopted":[81],"technique":[83,103,135],"for":[84,153],"dimensionality":[85],"reduction":[86],"non-time-series":[88],"transform":[90],"data":[94,115],"into":[95],"small":[96],"sets":[97],"Our":[101],"proposed":[102],"allows":[104],"analyst":[106],"easily":[108],"visualize":[109],"representations":[112],"detect":[117],"instance":[119],"which":[120],"has":[121],"potential":[123],"cause":[125],"test":[127],"failure.":[128],"We":[129],"demonstrated":[130],"robustness":[132],"subjecting":[137],"extracted":[139],"features":[140],"conventional":[143],"approach":[145],"such":[146],"as":[147],"Random":[148],"Forest.":[149],"The":[150,180],"datasets":[151,169,174],"used":[152],"evaluation":[155],"this":[157],"task":[158],"are":[159],"from":[160,188],"known":[162],"benchmarking":[163],"15":[165],"two":[171],"Motion":[172],"Caption":[173],"27":[176],"65":[178],"actions.":[179],"results":[182],"were":[183],"compared":[184],"outputs":[187],"Echo":[190],"State":[191],"Networks":[192,199],"(ESNs)":[193],"Bidirectional":[197],"Neural":[198],"(BRNNs).":[200]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
