{"id":53278,"date":"2019-11-28T00:00:00","date_gmt":"2019-11-27T21:00:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/blog_prohoster\/9-podhodov-dlya-vyyavleniya-anomalij"},"modified":"2020-02-18T14:01:10","modified_gmt":"2020-02-18T11:01:10","slug":"9-podhodov-dlya-vyyavleniya-anomalij","status":"publish","type":"post","link":"https:\/\/prohoster.info\/et\/blog\/news\/9-podhodov-dlya-vyyavleniya-anomalij","title":{"rendered":"9 l\u00e4henemist anomaaliate tuvastamiseks","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Uues eelnevas artiklis<\/a><\/noindex> Me r\u00e4\u00e4kisime ajavahemike prognoosimisest. Loogiline j\u00e4tk oleks artikkel anomaaliate tuvastamisest. <\/p>\n

Rakendamine<\/h3>\n

\nAnomaaliate tuvastamist kasutatakse sellistes valdkondades nagu: <\/p>\n

1) Seadmete rikete ennustamine<\/h3>\n

\nNii olid 2010. aastal Iraani tsentrifugid r\u00fcnnaku all Stuxnet viiruse t\u00f5ttu, mis kehtestas seadmetele mitteoptimaalse t\u00f6\u00f6re\u017eiimi ja rajas osa seadmetest kiiret kulumist. <\/p>\n

Kui seadmetes oleks kasutatud anomaaliaotsingu algoritme, oleks riketest tingitud seisakute olukord v\u00f5imalik v\u00e4ltida. <\/p>\n

\"9
\n<\/a><\/noindex>
\nAnomaaliate otsimine seadmete t\u00f6\u00f6s ei piirdu ainult tuumaenergeetikaga, vaid ulatub ka metallurgia ja lennuturbiinide t\u00f6\u00f6sse. Ning teistesse valdkondadesse, kus prognoosimisd\u00fcnaamika kasutamine on odavam kui ootamatute riketega seotud kaotused.<\/p>\n

2) Pettuste ennustamine<\/h3>\n

\nKui summad, mida te t\u00f5husa kasutamise eest Podolskis kulutate, kaovad Albaanias, siis v\u00f5ib olla vajalik tehingute t\u00e4iendav kontroll.<\/p>\n

3) Anomaalsete tarbimismustrite tuvastamine<\/h3>\n

\nKui osa klientidest n\u00e4itab anomaalset k\u00e4itumist, v\u00f5ib seal olla probleem, millest te ei tea.<\/p>\n

4) Anomaalse n\u00f5udluse ja koormuse tuvastamine<\/h3>\n

\nKui FMCG kaupluse m\u00fc\u00fck on langenud allapoole prognoosi usalduspiiri, tasub selgitada, mis toimub.<\/p>\n

Anomaaliate tuvastamise l\u00e4henemisviisid<\/h2>\n

<\/p>\n

1) \u00dche klassi tugi-vektorite meetod One-Class SVM<\/h3>\n

\nSobib, kui treeningkomplekt j\u00e4rgib normaaljaotust ja testkomplekt sisaldab anomaaliaid.<\/p>\n

\u00dche klassi tugi-vektorite meetod loob koordinaatide alguse \u00fcmber mittelineaarse pinna. V\u00f5ib m\u00e4\u00e4rata k\u00fcnnise, mille alusel andmeid anomaalseteks pidada.<\/p>\n

K\u00e4esoleva DATA4 meeskonna kogemuste kohaselt on One-Class SVM k\u00f5ige sagedamini kasutatav algoritm anomaaliate otsimise \u00fclesande lahendamiseks. <\/p>\n

\"9
\n<\/p>\n

2) Isolatsioonimetsa meetod \u2013 isolate forest<\/h3>\n

\nJuhuslikul viisil puude ehitamine \u00f5nnestub, et v\u00e4ljaj\u00e4tmise korral satub see lehtede varakult (madala puu s\u00fcgavuse juures), seega on anomaaliate eristamine lihtsam. Anomaalsete v\u00e4\u00e4rtuste eristamine toimub algoritmi t\u00f6\u00f6 alguses.<\/p>\n

\"9
\n<\/p>\n

3) Elliptne \u00fcmbris ja statistilised meetodid<\/h3>\n

\nKasutatakse, kui andmed on tavaliselt jaotatud. Mida l\u00e4hemal m\u00f5\u00f5tmine jaotuse sabale, seda enam on v\u00e4\u00e4rtus anomaalne.<\/p>\n

Selle klassi alla kuuluvad ka teised statistilised meetodid.<\/p>\n

\"9
\n
\n\"9
\nPilt saidilt dyakonov.org<\/i><\/p>\n

4) Metrilised meetodid<\/h3>\n

\nMeetoditesse kuuluvad sellised algoritmid nagu k l\u00e4himat naabrit, k-s l\u00e4him naaber, ABOD (nurgap\u00f5hine ebatavaliste tuvastamine) v\u00f5i LOF (kohalik ebatavaline tegur).<\/p>\n

Sobivad, kui v\u00e4\u00e4rtuste vahe arvulistes tunnustes on v\u00f5rdsed v\u00f5i normeeritud (et mitte m\u00f5\u00f5ta boa papagoode j\u00e4rgi). <\/p>\n

K l\u00e4himate naabrite algoritm eeldab, et normaalsed v\u00e4\u00e4rtused asuvad teatud piirkonnas mitmem\u00f5\u00f5tmelises ruumis ja kaugus anomaaliatest on suurem kui eraldus h\u00fcperpinna juurde. <\/p>\n

\"9
\n<\/p>\n

5) Klastrimeetodid<\/h3>\n

\nKlastrimeetodite olemus seisneb selles, et kui v\u00e4\u00e4rtus on klastrite keskpunkte kaugemal kui teatud suurus, v\u00f5ib seda v\u00e4\u00e4rtust pidada ebatavaliseks. <\/p>\n

Peamine on kasutada algoritmi, mis klastrib andmeid \u00f5igesti, mis s\u00f5ltub konkreetsetest \u00fclesannetest. <\/p>\n

\"9
\n<\/p>\n

6) Peamiste komponentide meetod<\/h3>\n

\nSobib kohtades, kus tuvastatakse suurima dispersiooni muutumise suundi.<\/p>\n

7) Aja rea prognoosimise algoritmid<\/h3>\n

\nIdee seisneb selles, et kui v\u00e4\u00e4rtus \u00fcletab usalduspiiri prognooside osas, peetakse v\u00e4\u00e4rtust ebatavaliseks. Aja rea prognoosimiseks kasutatakse selliseid algoritme nagu kolmekordne silumine, S(ARIMA), busteerimine jne.<\/p>\n

Aja rea prognoosimiste algoritmidest r\u00e4\u00e4giti eelmises artiklis.<\/p>\n

\"9
\n<\/p>\n

8) Juhendatud \u00f5ppimine (regressioon, klassifitseerimine)<\/h3>\n

\nKui andmed seda lubavad, kasutame algoritme, alates lineaarse regressioonist ja l\u00f5petades korduvate v\u00f5rkudega. M\u00e4\u00e4rame vahe prognoosi ja tegeliku v\u00e4\u00e4rtuse vahel ning teeme j\u00e4relduse, kui palju andmed normist k\u00f5rvale kalduvad. Oluline on, et algoritm omaks piisavalt \u00fcldistamisv\u00f5imet ning treeningu n\u00e4idis ei sisaldaks ebatavalisi v\u00e4\u00e4rtusi.<\/p>\n

9) Mudelitestimist<\/h3>\n

\nL\u00e4heneme anomaaliate tuvastamise \u00fclesandele nagu soovituste otsimise \u00fclesandele. Laotame oma tunnuste maatriksi SVD v\u00f5i faktoriseerimismasinate abil, ja v\u00e4\u00e4rtused uues maatriksis, mis oluliselt erinevad algsetest, loeme ebatavaliseks. <\/p>\n

\"9
\n
\nPilt saidilt dyakonov.org<\/i><\/p>\n

Kokkuv\u00f5te<\/h3>\n

\nSelles artiklis vaatasime \u00fcle peamised l\u00e4henemisviisid anomaaliate tuvastamiseks. <\/p>\n

Anomaaliate leidmine on paljuski kunst. Ei ole ideaalset algoritmi ega l\u00e4henemist, mille kasutamine lahendaks k\u00f5ik probleemid. Sageli kasutatakse konkreetse juhtumi lahendamiseks meetodite komplekti. Anomaaliate leidmine toimub \u00fcheklassiliste tugivektorite meetodi, isolatsiooni metsade, m\u00f5\u00f5dikute ja klastermeetodite, samuti peamiste komponentide ja ajareadude prognoosimise abil.<\/p>\n

Kui teate teisi meetodeid, kirjutage neist artikli kommentaarides.
\n
Allikas: habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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