{"id":182898,"date":"2022-12-07T16:36:00","date_gmt":"2022-12-07T21:36:00","guid":{"rendered":"https:\/\/ibkrcampus.com\/traders-insight\/r-code-creating-lagged-xs-and-y-for-supervised-learning\/"},"modified":"2023-02-23T12:18:09","modified_gmt":"2023-02-23T17:18:09","slug":"r-code-creating-lagged-xs-and-y-for-supervised-learning","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/r-code-creating-lagged-xs-and-y-for-supervised-learning\/","title":{"rendered":"R Code: Creating Lagged Xs and Y for Supervised Learning"},"content":{"rendered":"\n<p>This post shows a simple R code to create various lagged time series and concatenate them with the original time series. This can be used frequently when preprocessing time series data for machine\/deep learning models.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-creating-lagged-xs-and-y\">Creating lagged Xs and y<\/h2>\n\n\n\n<p>Time series and its various lagged one are used as input variables for supervised machine or deep learning models. The following R code generates this concatenation of a set of lagged and time-t variables of the time series.<\/p>\n\n\n\n<p>As an output format, Xs (lagged variables) are followed by y since it is relatively easy to access X by using &#8220;<strong>1:nk<\/strong>&#8221; rather than &#8220;2:(nk+1)&#8221;.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>#========================================================#\n# Quantitative ALM, Financial Econometrics &amp; Derivatives \n# ML\/DL using R, Python, Tensorflow by Sang-Heon Lee \n#\n# https:\/\/kiandlee.blogspot.com\n#--------------------------------------------------------#\n# Create lagged Xs and y for supervised learning\n#========================================================#\n \ngraphics.off(); rm(list = ls())\n \n#===============================================\n# function for creating lagged Xs and y\n#===============================================\n \nfunc_lagged_Xs_y &lt;- function(y, k, nmx = \"x\"){\n \n    nk &lt;- length(k); ny &lt;- length(y)\n    df &lt;- as.data.frame(matrix(nrow=ny, ncol=nk))\n    colnames(df) &lt;- paste0(nmx,k); df$y = y\n    \n    for(i in 1:nk) \n        df&#91;(1+k&#91;i]):ny,i] &lt;- y&#91;1:(ny-k&#91;i])]\n    \n    return(df&#91;(max(k)+1):ny,])\n}\n \n#-----------------------\n# sample data\n#-----------------------\ny &lt;- c(31.12, 27.95, 30.67, 27.18, 21.89, 19.90, 21.58, \n       18.69, 20.31, 21.89, 19.29, 20.57, 21.57, 22.87, \n       21.01, 18.63, 17.96, 17.68, 17.54, 16.95, 17.33)\n \n#-----------------------\n# test cases\n#-----------------------\nXy1 = func_lagged_Xs_y(y, k=1:10)\nXy1\n \nXy2 = func_lagged_Xs_y(y, k=c(1,3,5), nmx = \"XX\")\nXy2<\/code><\/pre>\n\n\n\n<p>As expected, the results of two examples in the above R code are as follows.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" data-src=\"\/campus\/wp-content\/uploads\/sites\/2\/2023\/02\/Xy_cases-sh-fintech.png\" alt=\" class=\" class=\"wp-image-169195 lazyload\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" \/><\/figure>\n\n\n\n<p><em>Visit SH Fintech Modeling for additional insight on this topic: <a href=\"https:\/\/kiandlee.blogspot.com\/2022\/10\/r-code-creating-lagged-xs-and-y-for.html\">https:\/\/kiandlee.blogspot.com\/2022\/10\/r-code-creating-lagged-xs-and-y-for.html<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This post shows a simple R code to create various lagged time series and concatenate them with the original time series. This can be used frequently when preprocessing time series data for machine\/deep learning models.<\/p>\n","protected":false},"author":662,"featured_media":182900,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,338,350,341,344,342],"tags":[2105,852,487,9587],"contributors-categories":[13728],"class_list":{"0":"post-182898","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-programing-languages","9":"category-ibkr-quant-news","10":"category-quant-asia-pacific","11":"category-quant-development","12":"category-quant-regions","13":"category-r-development","14":"tag-deep-learning","15":"tag-machine-learning","16":"tag-r","17":"tag-supervised-learning","18":"contributors-categories-sh-fintech-modeling"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.9 (Yoast SEO v27.4) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>R Code: Creating Lagged Xs and Y for Supervised Learning<\/title>\n<meta name=\"description\" content=\"This post shows a simple R code to create various lagged time series and concatenate them with the original time series. This can be used frequently...\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.interactivebrokers.com\/campus\/wp-json\/wp\/v2\/posts\/182898\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"R Code: Creating Lagged Xs and Y for Supervised Learning\" \/>\n<meta property=\"og:description\" content=\"This post shows a simple R code to create various lagged time series and concatenate them with the original time series. This can be used frequently when preprocessing time series data for machine\/deep learning models.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/r-code-creating-lagged-xs-and-y-for-supervised-learning\/\" \/>\n<meta property=\"og:site_name\" content=\"IBKR Campus US\" \/>\n<meta property=\"article:published_time\" content=\"2022-12-07T21:36:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-02-23T17:18:09+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2023\/02\/machine-learning-sphere.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"900\" \/>\n\t<meta property=\"og:image:height\" content=\"550\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Sang-Heon Lee\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"R Code: Creating Lagged Xs and Y for Supervised Learning\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Sang-Heon Lee\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\n\t    \"@context\": \"https:\\\/\\\/schema.org\",\n\t    \"@graph\": [\n\t        {\n\t            \"@type\": \"NewsArticle\",\n\t            \"@id\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/ibkr-quant-news\\\/r-code-creating-lagged-xs-and-y-for-supervised-learning\\\/#article\",\n\t            \"isPartOf\": {\n\t                \"@id\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/ibkr-quant-news\\\/r-code-creating-lagged-xs-and-y-for-supervised-learning\\\/\"\n\t            },\n\t            \"author\": {\n\t                \"name\": \"Sang-Heon Lee\",\n\t                \"@id\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/#\\\/schema\\\/person\\\/0a959ff9de7f0465a07baa1fe1ae0200\"\n\t            },\n\t            \"headline\": \"R Code: Creating Lagged Xs and Y for Supervised Learning\",\n\t            \"datePublished\": \"2022-12-07T21:36:00+00:00\",\n\t            \"dateModified\": \"2023-02-23T17:18:09+00:00\",\n\t            \"mainEntityOfPage\": {\n\t                \"@id\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/ibkr-quant-news\\\/r-code-creating-lagged-xs-and-y-for-supervised-learning\\\/\"\n\t            },\n\t            \"wordCount\": 145,\n\t            \"publisher\": {\n\t                \"@id\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/#organization\"\n\t            },\n\t            \"image\": {\n\t                \"@id\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/ibkr-quant-news\\\/r-code-creating-lagged-xs-and-y-for-supervised-learning\\\/#primaryimage\"\n\t            },\n\t            \"thumbnailUrl\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/wp-content\\\/uploads\\\/sites\\\/2\\\/2023\\\/02\\\/machine-learning-sphere.jpg\",\n\t            \"keywords\": [\n\t                \"Deep Learning\",\n\t                \"Machine Learning\",\n\t                \"R\",\n\t                \"Supervised Learning\"\n\t            ],\n\t            \"articleSection\": [\n\t                \"Data Science\",\n\t                \"Programming Languages\",\n\t                \"Quant\",\n\t                \"Quant Asia Pacific\",\n\t                \"Quant Development\",\n\t                \"Quant Regions\",\n\t                \"R Development\"\n\t            ],\n\t            \"inLanguage\": \"en-US\"\n\t        },\n\t        {\n\t            \"@type\": \"WebPage\",\n\t            \"@id\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/ibkr-quant-news\\\/r-code-creating-lagged-xs-and-y-for-supervised-learning\\\/\",\n\t            \"url\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/ibkr-quant-news\\\/r-code-creating-lagged-xs-and-y-for-supervised-learning\\\/\",\n\t            \"name\": \"R Code: Creating Lagged Xs and Y for Supervised Learning\",\n\t            \"isPartOf\": {\n\t                \"@id\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/#website\"\n\t            },\n\t            \"primaryImageOfPage\": {\n\t                \"@id\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/ibkr-quant-news\\\/r-code-creating-lagged-xs-and-y-for-supervised-learning\\\/#primaryimage\"\n\t            },\n\t            \"image\": {\n\t                \"@id\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/ibkr-quant-news\\\/r-code-creating-lagged-xs-and-y-for-supervised-learning\\\/#primaryimage\"\n\t            },\n\t            \"thumbnailUrl\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/wp-content\\\/uploads\\\/sites\\\/2\\\/2023\\\/02\\\/machine-learning-sphere.jpg\",\n\t            \"datePublished\": \"2022-12-07T21:36:00+00:00\",\n\t            \"dateModified\": \"2023-02-23T17:18:09+00:00\",\n\t            \"description\": \"This post shows a simple R code to create various lagged time series and concatenate them with the original time series. This can be used frequently when preprocessing time series data for machine\\\/deep learning models.\",\n\t            \"inLanguage\": \"en-US\",\n\t            \"potentialAction\": [\n\t                {\n\t                    \"@type\": \"ReadAction\",\n\t                    \"target\": [\n\t                        \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/ibkr-quant-news\\\/r-code-creating-lagged-xs-and-y-for-supervised-learning\\\/\"\n\t                    ]\n\t                }\n\t            ]\n\t        },\n\t        {\n\t            \"@type\": \"ImageObject\",\n\t            \"inLanguage\": \"en-US\",\n\t            \"@id\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/ibkr-quant-news\\\/r-code-creating-lagged-xs-and-y-for-supervised-learning\\\/#primaryimage\",\n\t            \"url\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/wp-content\\\/uploads\\\/sites\\\/2\\\/2023\\\/02\\\/machine-learning-sphere.jpg\",\n\t            \"contentUrl\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/wp-content\\\/uploads\\\/sites\\\/2\\\/2023\\\/02\\\/machine-learning-sphere.jpg\",\n\t            \"width\": 900,\n\t            \"height\": 550,\n\t            \"caption\": \"R Code: Creating Lagged Xs and Y for Supervised Learning\"\n\t        },\n\t        {\n\t            \"@type\": \"WebSite\",\n\t            \"@id\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/#website\",\n\t            \"url\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/\",\n\t            \"name\": \"IBKR Campus US\",\n\t            \"description\": \"Financial Education from Interactive Brokers\",\n\t            \"publisher\": {\n\t                \"@id\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/#organization\"\n\t            },\n\t            \"potentialAction\": [\n\t                {\n\t                    \"@type\": \"SearchAction\",\n\t                    \"target\": {\n\t                        \"@type\": \"EntryPoint\",\n\t                        \"urlTemplate\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/?s={search_term_string}\"\n\t                    },\n\t                    \"query-input\": {\n\t                        \"@type\": \"PropertyValueSpecification\",\n\t                        \"valueRequired\": true,\n\t                        \"valueName\": \"search_term_string\"\n\t                    }\n\t                }\n\t            ],\n\t            \"inLanguage\": \"en-US\"\n\t        },\n\t        {\n\t            \"@type\": \"Organization\",\n\t            \"@id\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/#organization\",\n\t            \"name\": \"Interactive Brokers\",\n\t            \"alternateName\": \"IBKR\",\n\t            \"url\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/\",\n\t            \"logo\": {\n\t                \"@type\": \"ImageObject\",\n\t                \"inLanguage\": \"en-US\",\n\t                \"@id\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/#\\\/schema\\\/logo\\\/image\\\/\",\n\t                \"url\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/wp-content\\\/uploads\\\/sites\\\/2\\\/2024\\\/05\\\/ibkr-campus-logo.jpg\",\n\t                \"contentUrl\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/wp-content\\\/uploads\\\/sites\\\/2\\\/2024\\\/05\\\/ibkr-campus-logo.jpg\",\n\t                \"width\": 669,\n\t                \"height\": 669,\n\t                \"caption\": \"Interactive Brokers\"\n\t            },\n\t            \"image\": {\n\t                \"@id\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/#\\\/schema\\\/logo\\\/image\\\/\"\n\t            },\n\t            \"publishingPrinciples\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/about-ibkr-campus\\\/\",\n\t            \"ethicsPolicy\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/cyber-security-notice\\\/\"\n\t        },\n\t        {\n\t            \"@type\": \"Person\",\n\t            \"@id\": \"https:\\\/\\\/ibkrcampus.com\\\/campus\\\/#\\\/schema\\\/person\\\/0a959ff9de7f0465a07baa1fe1ae0200\",\n\t            \"name\": \"Sang-Heon Lee\",\n\t            \"url\": \"https:\\\/\\\/www.interactivebrokers.com\\\/campus\\\/author\\\/sang-heonlee\\\/\"\n\t        }\n\t    ]\n\t}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"R Code: Creating Lagged Xs and Y for Supervised Learning","description":"This post shows a simple R code to create various lagged time series and concatenate them with the original time series. This can be used frequently...","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.interactivebrokers.com\/campus\/wp-json\/wp\/v2\/posts\/182898\/","og_locale":"en_US","og_type":"article","og_title":"R Code: Creating Lagged Xs and Y for Supervised Learning","og_description":"This post shows a simple R code to create various lagged time series and concatenate them with the original time series. This can be used frequently when preprocessing time series data for machine\/deep learning models.","og_url":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/r-code-creating-lagged-xs-and-y-for-supervised-learning\/","og_site_name":"IBKR Campus US","article_published_time":"2022-12-07T21:36:00+00:00","article_modified_time":"2023-02-23T17:18:09+00:00","og_image":[{"width":900,"height":550,"url":"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2023\/02\/machine-learning-sphere.jpg","type":"image\/jpeg"}],"author":"Sang-Heon Lee","twitter_card":"summary_large_image","twitter_title":"R Code: Creating Lagged Xs and Y for Supervised Learning","twitter_misc":{"Written by":"Sang-Heon Lee","Est. reading time":"2 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"NewsArticle","@id":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/r-code-creating-lagged-xs-and-y-for-supervised-learning\/#article","isPartOf":{"@id":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/r-code-creating-lagged-xs-and-y-for-supervised-learning\/"},"author":{"name":"Sang-Heon Lee","@id":"https:\/\/ibkrcampus.com\/campus\/#\/schema\/person\/0a959ff9de7f0465a07baa1fe1ae0200"},"headline":"R Code: Creating Lagged Xs and Y for Supervised Learning","datePublished":"2022-12-07T21:36:00+00:00","dateModified":"2023-02-23T17:18:09+00:00","mainEntityOfPage":{"@id":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/r-code-creating-lagged-xs-and-y-for-supervised-learning\/"},"wordCount":145,"publisher":{"@id":"https:\/\/ibkrcampus.com\/campus\/#organization"},"image":{"@id":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/r-code-creating-lagged-xs-and-y-for-supervised-learning\/#primaryimage"},"thumbnailUrl":"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2023\/02\/machine-learning-sphere.jpg","keywords":["Deep Learning","Machine Learning","R","Supervised Learning"],"articleSection":["Data Science","Programming Languages","Quant","Quant Asia Pacific","Quant Development","Quant Regions","R Development"],"inLanguage":"en-US"},{"@type":"WebPage","@id":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/r-code-creating-lagged-xs-and-y-for-supervised-learning\/","url":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/r-code-creating-lagged-xs-and-y-for-supervised-learning\/","name":"R Code: Creating Lagged Xs and Y for Supervised Learning","isPartOf":{"@id":"https:\/\/ibkrcampus.com\/campus\/#website"},"primaryImageOfPage":{"@id":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/r-code-creating-lagged-xs-and-y-for-supervised-learning\/#primaryimage"},"image":{"@id":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/r-code-creating-lagged-xs-and-y-for-supervised-learning\/#primaryimage"},"thumbnailUrl":"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2023\/02\/machine-learning-sphere.jpg","datePublished":"2022-12-07T21:36:00+00:00","dateModified":"2023-02-23T17:18:09+00:00","description":"This post shows a simple R code to create various lagged time series and concatenate them with the original time series. This can be used frequently when preprocessing time series data for machine\/deep learning models.","inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/r-code-creating-lagged-xs-and-y-for-supervised-learning\/"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/r-code-creating-lagged-xs-and-y-for-supervised-learning\/#primaryimage","url":"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2023\/02\/machine-learning-sphere.jpg","contentUrl":"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2023\/02\/machine-learning-sphere.jpg","width":900,"height":550,"caption":"R Code: Creating Lagged Xs and Y for Supervised Learning"},{"@type":"WebSite","@id":"https:\/\/ibkrcampus.com\/campus\/#website","url":"https:\/\/ibkrcampus.com\/campus\/","name":"IBKR Campus US","description":"Financial Education from Interactive Brokers","publisher":{"@id":"https:\/\/ibkrcampus.com\/campus\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/ibkrcampus.com\/campus\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/ibkrcampus.com\/campus\/#organization","name":"Interactive Brokers","alternateName":"IBKR","url":"https:\/\/ibkrcampus.com\/campus\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/ibkrcampus.com\/campus\/#\/schema\/logo\/image\/","url":"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2024\/05\/ibkr-campus-logo.jpg","contentUrl":"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2024\/05\/ibkr-campus-logo.jpg","width":669,"height":669,"caption":"Interactive Brokers"},"image":{"@id":"https:\/\/ibkrcampus.com\/campus\/#\/schema\/logo\/image\/"},"publishingPrinciples":"https:\/\/www.interactivebrokers.com\/campus\/about-ibkr-campus\/","ethicsPolicy":"https:\/\/www.interactivebrokers.com\/campus\/cyber-security-notice\/"},{"@type":"Person","@id":"https:\/\/ibkrcampus.com\/campus\/#\/schema\/person\/0a959ff9de7f0465a07baa1fe1ae0200","name":"Sang-Heon Lee","url":"https:\/\/www.interactivebrokers.com\/campus\/author\/sang-heonlee\/"}]}},"jetpack_featured_media_url":"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2023\/02\/machine-learning-sphere.jpg","_links":{"self":[{"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/posts\/182898","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/users\/662"}],"replies":[{"embeddable":true,"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/comments?post=182898"}],"version-history":[{"count":0,"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/posts\/182898\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/media\/182900"}],"wp:attachment":[{"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/media?parent=182898"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/categories?post=182898"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/tags?post=182898"},{"taxonomy":"contributors-categories","embeddable":true,"href":"https:\/\/ibkrcampus.com\/campus\/wp-json\/wp\/v2\/contributors-categories?post=182898"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}