{"id":879,"date":"2023-07-28T11:11:00","date_gmt":"2023-07-28T11:11:00","guid":{"rendered":"https:\/\/statorials.org\/id\/python-autokorelasi\/"},"modified":"2023-07-28T11:11:00","modified_gmt":"2023-07-28T11:11:00","slug":"python-autokorelasi","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/python-autokorelasi\/","title":{"rendered":"Cara menghitung autokorelasi dengan python"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>Autokorelasi<\/strong> mengukur tingkat kemiripan antara deret waktu dan versi yang tertinggal selama interval waktu yang berurutan.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kadang-kadang juga disebut &#8220;korelasi serial&#8221; atau &#8220;korelasi tertinggal&#8221; karena mengukur hubungan antara nilai variabel saat ini dan nilai historisnya.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ketika autokorelasi dalam deret waktu tinggi, prediksi nilai masa depan menjadi mudah hanya dengan mengacu pada nilai masa lalu.<\/span><\/p>\n<h3> <strong>Cara Menghitung Autokorelasi dengan Python<\/strong><\/h3>\n<p> <span style=\"color: #000000;\">Misalkan kita memiliki deret waktu berikut dengan Python yang menunjukkan nilai variabel tertentu selama 15 periode berbeda:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define data<\/span>\nx = [22, 24, 25, 25, 28, 29, 34, 37, 40, 44, 51, 48, 47, 50, 51]\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Kita dapat menghitung autokorelasi untuk setiap lag dalam deret waktu menggunakan <a href=\"https:\/\/www.statsmodels.org\/stable\/generated\/statsmodels.tsa.stattools.acf.html\" target=\"_blank\" rel=\"noopener noreferrer\">fungsi acf()<\/a> dari perpustakaan statsmodels:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> statsmodels.api <span style=\"color: #107d3f;\">as<\/span> sm\n\n<span style=\"color: #008080;\">#calculate autocorrelations<\/span>\nsm.tsa.acf(x)\n\narray([ 1. , 0.83174224, 0.65632458, 0.49105012, 0.27863962,\n        0.03102625, -0.16527446, -0.30369928, -0.40095465, -0.45823389,\n       -0.45047733])\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Cara menafsirkan hasilnya adalah sebagai berikut:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Autokorelasi pada lag 0 adalah <strong>1<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">Autokorelasi pada lag 1 sebesar <strong>0.8317<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">Autokorelasi pada lag 2 sebesar <strong>0.6563<\/strong> .<\/span><\/li>\n<li> <span style=\"color: #000000;\">Autokorelasi pada lag 3 sebesar <strong>0.4910<\/strong> .<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Dan seterusnya.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kita juga dapat menentukan jumlah lag yang akan digunakan dengan argumen <strong>nlags<\/strong> :<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>sm.tsa.acf(x, nlags= <span style=\"color: #008000;\">5<\/span> )\n\narray([1.0, 0.83174224, 0.65632458, 0.49105012, 0.27863962, 0.03102625])<\/strong><\/pre>\n<h3> <strong>Cara memplot fungsi autokorelasi dengan Python<\/strong><\/h3>\n<p> <span style=\"color: #000000;\">Kita dapat memplot fungsi autokorelasi untuk deret waktu dengan Python menggunakan <a href=\"https:\/\/www.statsmodels.org\/dev\/generated\/statsmodels.graphics.tsaplots.plot_acf.html\" target=\"_blank\" rel=\"noopener noreferrer\">fungsi tsaplots.plot_acf()<\/a> dari pustaka statsmodels:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">from<\/span> statsmodels.graphics <span style=\"color: #107d3f;\">import<\/span> tsaplots\n<span style=\"color: #107d3f;\">import<\/span> matplotlib.pyplot <span style=\"color: #107d3f;\">as<\/span> plt\n\n<span style=\"color: #008080;\">#plot autocorrelation function<\/span>\nfig = tsaplots.plot_acf(x, lags=10)\nplt.show()<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-9480 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/autocorrelationpython1.png\" alt=\"Fungsi autokorelasi dengan Python\" width=\"495\" height=\"343\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Sumbu x menampilkan jumlah lag dan sumbu y menampilkan autokorelasi pada jumlah lag tersebut. Secara default, plot dimulai pada lag = 0 dan autokorelasi akan selalu <strong>1<\/strong> pada lag = 0.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kita juga dapat memperbesar lag pertama dengan memilih untuk menggunakan lebih sedikit lag dengan argumen <strong>lags<\/strong> :<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">from<\/span> statsmodels.graphics <span style=\"color: #107d3f;\">import<\/span> tsaplots\n<span style=\"color: #107d3f;\">import<\/span> matplotlib.pyplot <span style=\"color: #107d3f;\">as<\/span> plt\n\n<span style=\"color: #008080;\">#plot autocorrelation function<\/span>\nfig = tsaplots.plot_acf(x, lags= <span style=\"color: #008000;\">5<\/span> )\nplt.show()<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-9481 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/autocorrelationpython2.png\" alt=\"Merencanakan Fungsi Autokorelasi dengan Python\" width=\"495\" height=\"329\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Anda juga dapat mengubah judul dan warna lingkaran yang digunakan dalam plot dengan argumen <strong>judul<\/strong> dan <strong>warna<\/strong> :<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">from<\/span> statsmodels.graphics <span style=\"color: #107d3f;\">import<\/span> tsaplots\n<span style=\"color: #107d3f;\">import<\/span> matplotlib.pyplot <span style=\"color: #107d3f;\">as<\/span> plt\n\n<span style=\"color: #008080;\">#plot autocorrelation function<\/span>\nfig = tsaplots.plot_acf(x, lags= <span style=\"color: #008000;\"><span style=\"color: #000000;\">5, color='g', title='Autocorrelation function'<\/span><\/span> <span style=\"color: #000000;\">)<\/span>\nplt.show()<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-9482 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/autocorrelationpython4.png\" alt=\"Fungsi autokorelasi dengan Python dengan judul khusus\" width=\"499\" height=\"342\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\"><em>Anda dapat menemukan lebih banyak tutorial Python di halaman ini .<\/em><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Autokorelasi mengukur tingkat kemiripan antara deret waktu dan versi yang tertinggal selama interval waktu yang berurutan. Kadang-kadang juga disebut &#8220;korelasi serial&#8221; atau &#8220;korelasi tertinggal&#8221; karena mengukur hubungan antara nilai variabel saat ini dan nilai historisnya. Ketika autokorelasi dalam deret waktu tinggi, prediksi nilai masa depan menjadi mudah hanya dengan mengacu pada nilai masa lalu. Cara [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Cara Menghitung Autokorelasi dengan Python - Statologi<\/title>\n<meta name=\"description\" content=\"Penjelasan sederhana tentang cara menghitung dan memplot fungsi autokorelasi dengan Python.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/id\/python-autokorelasi\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara Menghitung Autokorelasi dengan Python - Statologi\" \/>\n<meta property=\"og:description\" content=\"Penjelasan sederhana tentang cara menghitung dan memplot fungsi autokorelasi dengan Python.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/python-autokorelasi\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-28T11:11:00+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/autocorrelationpython1.png\" \/>\n<meta name=\"author\" content=\"Benjamin anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Ditulis oleh\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimasi waktu membaca\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 menit\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/id\/python-autokorelasi\/\",\"url\":\"https:\/\/statorials.org\/id\/python-autokorelasi\/\",\"name\":\"Cara Menghitung Autokorelasi dengan Python - Statologi\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-28T11:11:00+00:00\",\"dateModified\":\"2023-07-28T11:11:00+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Penjelasan sederhana tentang cara menghitung dan memplot fungsi autokorelasi dengan Python.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/python-autokorelasi\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/python-autokorelasi\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/python-autokorelasi\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara menghitung autokorelasi dengan python\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/id\/#website\",\"url\":\"https:\/\/statorials.org\/id\/\",\"name\":\"Statorials\",\"description\":\"Panduan anda untuk kompetensi statistik!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/id\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"id\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\",\"name\":\"Benjamin anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"id\",\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/image\/\",\"url\":\"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"caption\":\"Benjamin anderson\"},\"description\":\"Halo, saya Benjamin, pensiunan profesor statistika yang menjadi guru Statorial yang berdedikasi. 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