[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290639-105":3,"detail-sidebar-cat-0-en-105":80,"doc-detail-290639-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":73,"head_meta":75,"extra_data":77,"updated_unix":79},105,"lt","application-of-statistical-and-machine-learning-methods-for-export-data-forecasting-bachelors-thesis","Statistinių ir mašininio mokymosi metodų taikymas eksporto duomenų prognozavimui - Baigiamasis bakalauro darbas","","Baigiamajame bakalauro darbe nagrinėjamas statistinių ir mašininio mokymosi metodų taikymas eksporto duomenų prognozavimui. Darbe pateikiama prognozavimo metodų apžvalga, aptariami statistiniai, mašininio mokymosi, giliojo bei hibridiniai metodai, įvardijami papildomi rodikliai ir atliekama hiperparametrų paieška. Projektuojamas ir kuriamas prognozavimo sprendimas: taikomi ARIMA, SVR, trumpalaikės ir ilgalaikės atminties, SARIMA–SVR hibridiniai modeliai, naudojamos klaidos metrikos, normavimo metodai, duomenų paruošimo strategijos bei validacijos schemos.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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vidurkiai.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},290639,1789642251,{"code":4,"msg":81,"data":82},"success",[83,87,91,95,100,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},"Story & 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\nStatistinių ir mašininio mokymosi metodų taikymas eksporto  \nduomenų prognozavimui  \nApplication of Statistical and Machine Learning Methods for Export  \nData Forecasting  \nBaigiamasis bakalauro darbas  \nAtliko: Tomas Kasperavičius  \nVU el. p.: [tomas.kasperavicius@mif.stud.vu.lt](tomas.kasperavicius@mif.stud.vu.lt)  \n[Vadovas: Prof. Dr. Remigijus Paulavi](Vadovas: Prof. Dr. Remigijus Paulavi)č[ius](ius)  \nVilnius  \nTURINYS  \nSantrauka .....................................................................................................................................................3  \nSummary ......................................................................................................................................................4  \n1. ĮVADAS..................................................................................................................................................5  \n1.1. Darbo tikslas ...............................................................................................................................5  \n1.2. Darbo uždaviniai .........................................................................................................................5  \n2. ANALITINĖ DALIS ..................................................................................................................................6  \n2.1. Prognozavimo metodų apžvalga.................................................................................................6  \n2.1.1. Statistiniai metodai ............................................................................................................6  \n2.1.2. Mašininio mokymosi metodai ............................................................................................6  \n2.1.3. Giliojo mokymosi metodai .................................................................................................6  \n2.1.4. Hibridiniai metodai.............................................................................................................7  \n2.2. Papildomi rodikliai ......................................................................................................................7  \n2.3. Hiperparametrų paieška .............................................................................................................7  \n2.4. Duomenų kiekio didinimas .........................................................................................................7  \n2.5. Panašūs prognozavimo įrankiai ..................................................................................................8  \n3. METODINĖ DALIS..................................................................................................................................9  \n3.1. Naudoti duomenys......................................................................................................................9  \n3.1.1. Duomenų aibės ..................................................................................................................9  \n3.1.2. Išoriniai duomenys .............................................................................................................9  \n3.2. Naudoti metodai .........................................................................................................................9  \n3.2.1. Autoregresyvus integruotas slenkantis vidurkis (ARIMA) ..................................................9  \n3.2.2. Atraminių vektorių regresija (SVR) ...................................................................................10  \n3.2.3. Trumpalaikė ir ilgalaikė atmintis ......................................................................................11  \n3.2.4. SARIMA – SVR hibridinis modelis .....................................................................................11  \n3.3. Paklaidos skaičiavimo metrikos .................","cbCaihDjpXPNZZEq","https://ap.wps.com/l/cbCaihDjpXPNZZEq","pdf",3247904,44,"Lithuanian","en","# ĮVADAS\n## Darbo tikslas\n## Darbo uždaviniai\n# ANALITINĖ DALIS\n## Prognozavimo metodų apžvalga\n## Papildomi rodikliai\n## Hiperparametrų paieška\n## Duomenų kiekio didinimas\n## Panašūs prognozavimo įrankiai\n# METODINĖ DALIS\n## Naudoti duomenys\n## Naudoti metodai\n## Paklaidos skaičiavimo metrikos\n## Normavimo metodai\n## Naudotos strategijos\n## Naudoti įrankiai bei bibliotekos\n## Naudoti etaloniniai rezultatai\n## Kuriama prognozavimo sistema\n# ĮGYVENDINIMO DALIS\n## Papildomi rodikliai\n## Duomenų kiekio didinimas","[{\"question\":\"Koks šio darbo pagrindinis tikslas?\",\"answer\":\"Darbo tikslas – taikyti statistinius ir mašininio mokymosi metodus eksporto duomenų prognozavimui.\"},{\"question\":\"Kokie prognozavimo modeliai naudojami metodinėje dalyje?\",\"answer\":\"Naudojami ARIMA, SVR, trumpalaikės ir ilgalaikės atminties (LSTM) metodai, taip pat SARIMA–SVR hibridinis modelis.\"},{\"question\":\"Kaip organizuojamas duomenų paruošimas ir vertinimas?\",\"answer\":\"Darbe aptariamas duomenų kiekio didinimas, mokymo ir testavimo aibių sudarymas bei slenkanti į priekį validacija, taip pat slenkantys vidurkiai.\"}]","Statistinių ir mašininio mokymosi metodų taikymas eksporto duomenų prognozavimui - Baigiamasis bakalauro darbas | PDF",111]