{"id":475954,"date":"2023-08-09T07:24:43","date_gmt":"2023-08-09T07:24:43","guid":{"rendered":""},"modified":"2023-09-05T11:11:41","modified_gmt":"2023-09-05T11:11:41","slug":"autoregressive-integrated-moving-average-arima","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/autoregressive-integrated-moving-average-arima\/","title":{"rendered":"Otoregresif Entegre Hareketli Ortalama (ARIMA)"},"content":{"rendered":"<p>Otoregresif Entegre Hareketli Ortalama (ARIMA), temel bir istatistiksel model olarak zaman serisi tahmininde \u00f6nemli bir role sahiptir. K\u00f6kleri istatistiksel tahminin matemati\u011fine dayanan ARIMA, serideki \u00f6nceki veri noktalar\u0131na dayanarak gelecekteki veri noktalar\u0131n\u0131 tahmin etmek i\u00e7in \u00e7e\u015fitli sekt\u00f6rlerde yayg\u0131n olarak kullan\u0131lmaktad\u0131r.<\/p>\n<h2>ARIMA&#039;n\u0131n K\u00f6kenleri<\/h2>\n<p>ARIMA ilk olarak 1970&#039;lerin ba\u015f\u0131nda istatistik\u00e7iler George Box ve Gwilym Jenkins taraf\u0131ndan tan\u0131t\u0131ld\u0131. Geli\u015ftirme, otoregresif (AR) ve hareketli ortalama (MA) modelleri etraf\u0131nda yap\u0131lan daha \u00f6nceki \u00e7al\u0131\u015fmalara dayan\u0131yordu. Box ve Jenkins, fark alma kavram\u0131n\u0131 entegre ederek dura\u011fan olmayan zaman serilerini ele almay\u0131 ba\u015fard\u0131lar ve bu da ARIMA modelini ortaya \u00e7\u0131kard\u0131.<\/p>\n<h2>ARIMA&#039;y\u0131 Anlamak<\/h2>\n<p>ARIMA \u00fc\u00e7 temel y\u00f6ntemin birle\u015fimidir: Otoregresif (AR), Entegre (I) ve Hareketli Ortalama (MA). Bu y\u00f6ntemler zaman serisi verilerini analiz etmek ve tahmin etmek i\u00e7in kullan\u0131l\u0131r.<\/p>\n<ul>\n<li>\n<p><strong>Otoregresif (AR)<\/strong>: Bu y\u00f6ntem, bir g\u00f6zlem ile baz\u0131 gecikmeli g\u00f6zlemler (\u00f6nceki d\u00f6nemler) aras\u0131ndaki ba\u011f\u0131ml\u0131 ili\u015fkiyi kullan\u0131r.<\/p>\n<\/li>\n<li>\n<p><strong>Entegre (I)<\/strong>: Bu yakla\u015f\u0131m, zaman serisini dura\u011fan hale getirmek i\u00e7in g\u00f6zlemlerin fark\u0131n\u0131 almay\u0131 i\u00e7erir.<\/p>\n<\/li>\n<li>\n<p><strong>Hareketli Ortalama (MA)<\/strong>: Bu teknik, bir g\u00f6zlem ile gecikmeli g\u00f6zlemlere uygulanan hareketli ortalama modelinden kalan hata aras\u0131ndaki ba\u011f\u0131ml\u0131l\u0131\u011f\u0131 kullan\u0131r.<\/p>\n<\/li>\n<\/ul>\n<p>ARIMA modelleri genellikle ARIMA(p, d, q) olarak belirtilir; burada &#039;p&#039; AR b\u00f6l\u00fcm\u00fcn\u00fcn s\u0131ras\u0131d\u0131r, &#039;d&#039; zaman serisini dura\u011fan hale getirmek i\u00e7in gereken fark s\u0131ras\u0131d\u0131r ve &#039;q&#039; s\u0131rad\u0131r MA k\u0131sm\u0131ndan.<\/p>\n<h2>ARIMA&#039;n\u0131n \u0130\u00e7 Yap\u0131s\u0131 ve \u00c7al\u0131\u015fmas\u0131<\/h2>\n<p>ARIMA&#039;n\u0131n yap\u0131s\u0131 \u00fc\u00e7 b\u00f6l\u00fcmden olu\u015fur: AR, I ve MA. Her b\u00f6l\u00fcm veri analizinde belirli bir rol oynar:<\/p>\n<ul>\n<li><strong>AR k\u0131sm\u0131<\/strong> ge\u00e7mi\u015f d\u00f6nem de\u011ferlerinin cari d\u00f6nem \u00fczerindeki etkisini \u00f6l\u00e7er.<\/li>\n<li><strong>ayr\u0131l\u0131yorum<\/strong> Veriyi dura\u011fan hale getirmek yani veriden trendi \u00e7\u0131karmak i\u00e7in kullan\u0131l\u0131r.<\/li>\n<li><strong>MA b\u00f6l\u00fcm\u00fc<\/strong> Bir g\u00f6zlem ile gecikmeli g\u00f6zlemlere uygulanan hareketli ortalama modelinden kalan hata aras\u0131ndaki ba\u011f\u0131ml\u0131l\u0131\u011f\u0131 i\u00e7erir.<\/li>\n<\/ul>\n<p>ARIMA modeli bir zaman serisine \u00fc\u00e7 a\u015famada uygulan\u0131r:<\/p>\n<ol>\n<li><strong>Tan\u0131lama<\/strong>: Fark alma s\u0131ras\u0131n\u0131n, &#039;d&#039; ve AR veya MA bile\u015fenlerinin s\u0131ras\u0131n\u0131n belirlenmesi.<\/li>\n<li><strong>Tahmin<\/strong>: Model belirlendikten sonra katsay\u0131lar\u0131 tahmin etmek i\u00e7in veriler modele uyarlan\u0131r.<\/li>\n<li><strong>Do\u011frulama<\/strong>: Tak\u0131lan modelin verilere iyi uyum sa\u011flad\u0131\u011f\u0131ndan emin olmak i\u00e7in kontrol edilir.<\/li>\n<\/ol>\n<h2>ARIMA&#039;n\u0131n Temel \u00d6zellikleri<\/h2>\n<ul>\n<li>ARIMA modelleri ge\u00e7mi\u015f ve mevcut verilere dayanarak gelecekteki veri noktalar\u0131n\u0131 tahmin edebilir.<\/li>\n<li>Dura\u011fan olmayan zaman serisi verilerini i\u015fleyebilir.<\/li>\n<li>Veriler net bir e\u011filim veya mevsimsel model g\u00f6sterdi\u011finde \u00f6zellikle etkilidir.<\/li>\n<li>ARIMA&#039;n\u0131n do\u011fru sonu\u00e7lar vermesi i\u00e7in b\u00fcy\u00fck miktarda veri gerekir.<\/li>\n<\/ul>\n<h2>ARIMA T\u00fcrleri<\/h2>\n<p>ARIMA modellerinin iki ana t\u00fcr\u00fc vard\u0131r:<\/p>\n<ol>\n<li>\n<p><strong>Sezonluk Olmayan ARIMA<\/strong>: ARIMA&#039;n\u0131n en basit \u015feklidir. Kesin d\u00f6ng\u00fcsel e\u011filimlerin bulunmad\u0131\u011f\u0131 mevsimsel olmayan veriler i\u00e7in kullan\u0131l\u0131r.<\/p>\n<\/li>\n<li>\n<p><strong>Mevsimsel ARIMA (SARIMA)<\/strong>: Modeldeki mevsimsel bir bile\u015feni a\u00e7\u0131k\u00e7a destekleyen ARIMA&#039;n\u0131n bir uzant\u0131s\u0131d\u0131r.<\/p>\n<\/li>\n<\/ol>\n<h2>ARIMA ve Problem \u00c7\u00f6zmenin Pratik Uygulamalar\u0131<\/h2>\n<p>ARIMA&#039;n\u0131n ekonomik tahmin, sat\u0131\u015f tahmini, borsa analizi ve daha fazlas\u0131n\u0131 i\u00e7eren \u00e7ok say\u0131da uygulamas\u0131 vard\u0131r.<\/p>\n<p>ARIMA&#039;da kar\u015f\u0131la\u015f\u0131lan yayg\u0131n sorunlardan biri, modelin e\u011fitim verilerine \u00e7ok yak\u0131n uyum sa\u011flad\u0131\u011f\u0131 ve yeni, g\u00f6r\u00fcnmeyen veriler \u00fczerinde d\u00fc\u015f\u00fck performans g\u00f6sterdi\u011fi a\u015f\u0131r\u0131 uyumdur. \u00c7\u00f6z\u00fcm, a\u015f\u0131r\u0131 uyumu \u00f6nlemek i\u00e7in \u00e7apraz do\u011frulama gibi tekniklerin kullan\u0131lmas\u0131nda yatmaktad\u0131r.<\/p>\n<h2>Benzer Y\u00f6ntemlerle Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u00d6zellik<\/th>\n<th>ARIMA<\/th>\n<th>\u00dcstel D\u00fczeltme<\/th>\n<th>Tekrarlayan Sinir A\u011f\u0131 (RNN)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Dura\u011fan olmayan verileri i\u015fler<\/td>\n<td>Evet<\/td>\n<td>HAYIR<\/td>\n<td>Evet<\/td>\n<\/tr>\n<tr>\n<td>Hatay\u0131, trendi ve mevsimselli\u011fi dikkate al\u0131r<\/td>\n<td>Evet<\/td>\n<td>Evet<\/td>\n<td>HAYIR<\/td>\n<\/tr>\n<tr>\n<td>B\u00fcy\u00fck veri k\u00fcmelerine ihtiya\u00e7 var<\/td>\n<td>Evet<\/td>\n<td>HAYIR<\/td>\n<td>Evet<\/td>\n<\/tr>\n<tr>\n<td>Yorumlama Kolayl\u0131\u011f\u0131<\/td>\n<td>Y\u00fcksek<\/td>\n<td>Y\u00fcksek<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>ARIMA&#039;n\u0131n Gelecek Perspektifleri<\/h2>\n<p>ARIMA, zaman serisi tahmini alan\u0131nda temel bir model olmaya devam ediyor. Daha do\u011fru tahminler i\u00e7in ARIMA&#039;n\u0131n makine \u00f6\u011frenme teknikleri ve yapay zeka teknolojileriyle entegrasyonu gelecek i\u00e7in \u00f6nemli bir trend.<\/p>\n<h2>Proxy Sunucular\u0131 ve ARIMA<\/h2>\n<p>Proxy sunucular\u0131, trafik tahmininde y\u00fck dengeleme ve sunucu kaynak tahsisinin y\u00f6netilmesine yard\u0131mc\u0131 olan ARIMA modellerinden potansiyel olarak yararlanabilir. Trafi\u011fi tahmin ederek proxy sunucular, optimum \u00e7al\u0131\u015fmay\u0131 sa\u011flamak i\u00e7in kaynaklar\u0131 dinamik olarak ayarlayabilir.<\/p>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.wiley.com\/en-us\/Time+Series+Analysis%3A+Forecasting+and+Control%2C+4th+Edition-p-9780470272848\" target=\"_new\" rel=\"noopener nofollow\">Box, GEP, Jenkins, GM ve Reinsel, GC (2008) Zaman Serisi Analizi: Tahmin ve Kontrol. Wiley.<\/a><\/li>\n<li><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417420302903\" target=\"_new\" rel=\"noopener nofollow\">Zaman Serisi Verileri i\u00e7in Ensemble Learning Insights ile ARIMA\/SARIMA ve LSTM kar\u015f\u0131la\u015ft\u0131rmas\u0131<\/a><\/li>\n<li><a href=\"https:\/\/www.mathworks.com\/help\/econ\/autoregressive-integrated-moving-average-arima.html\" target=\"_new\" rel=\"noopener nofollow\">Otoregresif Entegre Hareketli Ortalama (ARIMA) \u2013 MATLAB ve Simulink<\/a><\/li>\n<\/ul>","protected":false},"featured_media":467678,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-475954","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Autoregressive Integrated Moving Average (ARIMA): A Comprehensive Analysis<\/mark>","faq_items":[{"question":"What is the Autoregressive Integrated Moving Average (ARIMA)?","answer":"<p>Autoregressive Integrated Moving Average (ARIMA) is a statistical model used to analyze and forecast time series data. It combines three methods: Autoregressive (AR), Integrated (I), and Moving Average (MA).<\/p>"},{"question":"Who introduced the ARIMA model and when?","answer":"<p>The ARIMA model was introduced in the early 1970s by statisticians George Box and Gwilym Jenkins. The model extended earlier work around autoregressive (AR) and moving average (MA) models and introduced the concept of differencing to handle non-stationary time series.<\/p>"},{"question":"What are the three parts of the ARIMA model?","answer":"<p>The three parts of the ARIMA model are Autoregressive (AR), Integrated (I), and Moving Average (MA). The AR part measures the influence of past periods\u2019 values on the current period. The I part removes the trend from the data to make it stationary. The MA part incorporates the dependency between an observation and a residual error from a moving average model applied to lagged observations.<\/p>"},{"question":"What are the key features of ARIMA?","answer":"<p>ARIMA models can forecast future data points based on past and present data. They can handle time series data that are non-stationary and are particularly effective when data show a clear trend or seasonal pattern. However, ARIMA requires a large amount of data to yield accurate results.<\/p>"},{"question":"What are the types of ARIMA models?","answer":"<p>There are two main types of ARIMA models: Non-Seasonal ARIMA, used for non-seasonal data where there are no definitive cyclic trends, and Seasonal ARIMA (SARIMA), an extension of ARIMA that explicitly supports a seasonal component in the model.<\/p>"},{"question":"What problems are commonly encountered with ARIMA and how can they be solved?","answer":"<p>One common problem encountered with ARIMA is overfitting, where the model fits too closely to the training data and performs poorly on new, unseen data. Techniques such as cross-validation can be used to avoid overfitting.<\/p>"},{"question":"How is ARIMA relevant to proxy servers?","answer":"<p>Proxy servers could potentially benefit from ARIMA models in traffic prediction, helping to manage load balancing and server resource allocation. By predicting traffic, proxy servers can dynamically adjust resources to ensure optimal operation.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/475954","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/475954\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/467678"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=475954"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}