{"id":"https://openalex.org/W2616861977","doi":"https://doi.org/10.1504/ijcse.2017.10005023","title":"Fusion of statistical and machine learning approaches for time series prediction using earth observation data","display_name":"Fusion of statistical and machine learning approaches for time series prediction using earth observation data","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2616861977","doi":"https://doi.org/10.1504/ijcse.2017.10005023","mag":"2616861977"},"language":"en","primary_location":{"id":"doi:10.1504/ijcse.2017.10005023","is_oa":false,"landing_page_url":"https://doi.org/10.1504/ijcse.2017.10005023","pdf_url":null,"source":{"id":"https://openalex.org/S65043351","display_name":"International Journal of Computational Science and Engineering","issn_l":"1742-7185","issn":["1742-7185","1742-7193"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310317825","host_organization_name":"Inderscience Publishers","host_organization_lineage":["https://openalex.org/P4310317825"],"host_organization_lineage_names":["Inderscience Publishers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Science and Engineering","raw_type":"journal-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/A5110829919","display_name":"Ayush Bhatnagar","orcid":null},"institutions":[{"id":"https://openalex.org/I162827531","display_name":"Indian Institute of Technology Bombay","ror":"https://ror.org/02qyf5152","country_code":"IN","type":"education","lineage":["https://openalex.org/I162827531"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Ayush Bhatnagar","raw_affiliation_strings":["Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Bombay, Mumbai, Maharashtra, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Bombay, Mumbai, Maharashtra, India","institution_ids":["https://openalex.org/I162827531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110306829","display_name":"Shashikant Sharma","orcid":null},"institutions":[{"id":"https://openalex.org/I1289461252","display_name":"Indian Space Research Organisation","ror":"https://ror.org/00cwrns71","country_code":"IN","type":"government","lineage":["https://openalex.org/I1289461252","https://openalex.org/I3148377317"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Shashikant Sharma","raw_affiliation_strings":["Space Application Centre (ISRO), Ahmedabad, Gujarat, India"],"affiliations":[{"raw_affiliation_string":"Space Application Centre (ISRO), Ahmedabad, Gujarat, India","institution_ids":["https://openalex.org/I1289461252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003544175","display_name":"Pinkal Patel","orcid":null},"institutions":[{"id":"https://openalex.org/I165831266","display_name":"Nirma University","ror":"https://ror.org/05qkq7x38","country_code":"IN","type":"education","lineage":["https://openalex.org/I165831266"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Pinkal Patel","raw_affiliation_strings":["Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India","institution_ids":["https://openalex.org/I165831266"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008473098","display_name":"Sanjay Garg","orcid":"https://orcid.org/0000-0002-2279-9373"},"institutions":[{"id":"https://openalex.org/I165831266","display_name":"Nirma University","ror":"https://ror.org/05qkq7x38","country_code":"IN","type":"education","lineage":["https://openalex.org/I165831266"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sanjay Garg","raw_affiliation_strings":["Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India","institution_ids":["https://openalex.org/I165831266"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031097535","display_name":"K.P. Agrawal","orcid":"https://orcid.org/0000-0003-3759-2452"},"institutions":[{"id":"https://openalex.org/I165831266","display_name":"Nirma University","ror":"https://ror.org/05qkq7x38","country_code":"IN","type":"education","lineage":["https://openalex.org/I165831266"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"K.P. Agrawal","raw_affiliation_strings":["Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, Gujarat, India","institution_ids":["https://openalex.org/I165831266"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5110829919"],"corresponding_institution_ids":["https://openalex.org/I162827531"],"apc_list":null,"apc_paid":null,"fwci":0.3698,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.55123163,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"14","issue":"3","first_page":"255","last_page":"255"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9918000102043152,"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":0.9918000102043152,"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/T10320","display_name":"Neural Networks and Applications","score":0.9900000095367432,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9858999848365784,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.7986174821853638},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.7173165082931519},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.6467651128768921},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6376065611839294},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5511554479598999},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5108842253684998},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47739189863204956},{"id":"https://openalex.org/keywords/moving-average","display_name":"Moving average","score":0.46362876892089844},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.4183889627456665},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36066538095474243},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2014276087284088},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.1707000732421875}],"concepts":[{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.7986174821853638},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.7173165082931519},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.6467651128768921},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6376065611839294},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5511554479598999},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5108842253684998},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47739189863204956},{"id":"https://openalex.org/C175706884","wikidata":"https://www.wikidata.org/wiki/Q1130194","display_name":"Moving average","level":2,"score":0.46362876892089844},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.4183889627456665},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36066538095474243},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2014276087284088},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.1707000732421875},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","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:10.1504/ijcse.2017.10005023","is_oa":false,"landing_page_url":"https://doi.org/10.1504/ijcse.2017.10005023","pdf_url":null,"source":{"id":"https://openalex.org/S65043351","display_name":"International Journal of Computational Science and Engineering","issn_l":"1742-7185","issn":["1742-7185","1742-7193"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310317825","host_organization_name":"Inderscience Publishers","host_organization_lineage":["https://openalex.org/P4310317825"],"host_organization_lineage_names":["Inderscience Publishers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Computational Science and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","score":0.5799999833106995,"display_name":"Climate action"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1522237958","https://openalex.org/W1964357740","https://openalex.org/W1989082894","https://openalex.org/W1991505668","https://openalex.org/W2015037454","https://openalex.org/W2035538419","https://openalex.org/W2038182256","https://openalex.org/W2042843147","https://openalex.org/W2050902533","https://openalex.org/W2062421875","https://openalex.org/W2076941638","https://openalex.org/W2093771778","https://openalex.org/W2102417376","https://openalex.org/W2108859253","https://openalex.org/W2119821739","https://openalex.org/W2147280166","https://openalex.org/W2156909104","https://openalex.org/W2556533714"],"related_works":["https://openalex.org/W3190289737","https://openalex.org/W2567143433","https://openalex.org/W2073847544","https://openalex.org/W3175321409","https://openalex.org/W2620501840","https://openalex.org/W4312561791","https://openalex.org/W2389894046","https://openalex.org/W2215717369","https://openalex.org/W3115491726","https://openalex.org/W4313123484"],"abstract_inverted_index":{"This":[0,77],"paper":[1],"focuses":[2],"on":[3,96],"fusion":[4],"of":[5,15,23,75,98,123,140],"statistical":[6],"and":[7,27,125],"machine":[8,48],"learning":[9,49],"models":[10,124],"for":[11,130],"improving":[12],"the":[13,72,138],"accuracy":[14],"time":[16,36,45,61,129],"series":[17,37,62],"prediction.":[18],"Statistical":[19],"model":[20,143],"like":[21],"integration":[22],"auto":[24,82],"regressive":[25,83],"(AR)":[26],"moving":[28,85],"average":[29,86],"(MA)":[30],"is":[31,144],"capable":[32],"to":[33,80,105],"handle":[34,57],"non-stationary":[35],"but":[38],"it":[39,69],"can":[40,56],"deal":[41],"with":[42,64,88],"only":[43],"single":[44],"series.":[46],"The":[47],"approach":[50],"[i.e.":[51],"support":[52],"vector":[53],"regression":[54],"(SVR)]":[55],"dependency":[58],"among":[59],"different":[60,132],"along":[63],"nonlinear":[65],"separable":[66],"domains,":[67],"however":[68],"cannot":[70],"incorporate":[71],"past":[73],"behaviour":[74],"time-series.":[76],"led":[78],"us":[79],"hybridise":[81],"integrated":[84],"(ARIMA)":[87],"SVR":[89],"model,":[90],"where":[91],"focus":[92],"has":[93,107],"been":[94,108],"given":[95],"minimisation":[97],"forecast":[99],"error":[100],"using":[101],"residuals.":[102],"Issue":[103],"related":[104],"scalability":[106],"handled":[109],"by":[110],"taking":[111],"suitable":[112],"representative":[113],"samples":[114],"from":[115],"each":[116],"sub-areas":[117],"which":[118],"helped":[119],"in":[120,128],"reducing":[121],"number":[122],"drastic":[126],"reduction":[127],"tuning":[131],"parameters.":[133],"Results":[134],"obtained":[135],"show":[136],"that":[137],"performance":[139],"proposed":[141],"hybrid":[142],"better":[145],"than":[146],"individual":[147],"models.":[148]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}