{"id":176879,"date":"2024-01-21T10:01:15","date_gmt":"2024-01-21T09:01:15","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=176879"},"modified":"2024-01-19T11:00:30","modified_gmt":"2024-01-19T10:00:30","slug":"time-series-daniel-can-you-tell-us-about-them","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/time-series-daniel-can-you-tell-us-about-them","title":{"rendered":"Time series: Daniel, can you tell us about them?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"176879\" class=\"elementor elementor-176879\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ac53ad8 e-flex e-con-boxed e-con\" data-id=\"ac53ad8\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-b35e153 e-con-full e-flex e-con\" data-id=\"b35e153\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d8ac437 elementor-widget elementor-widget-heading\" data-id=\"d8ac437\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">New appointment with Daniel, the technical support for DataScientest.&nbsp; the data science expert who accompanies learners throughout their training courses. Today, he talks to us about time series.\nTime series is one of the most widely studied topics in data science. In this article, you'll discover the main components of a time series.<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-de37245 e-flex e-con-boxed e-con\" data-id=\"de37245\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-f58cb73 e-con-full e-flex e-con\" data-id=\"f58cb73\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0022aab elementor-widget elementor-widget-heading\" data-id=\"0022aab\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What is a time series?\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1c477db e-flex e-con-boxed e-con\" data-id=\"1c477db\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-cbff6fd e-con-full e-flex e-con\" data-id=\"cbff6fd\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-35d0e10 elementor-widget elementor-widget-text-editor\" data-id=\"35d0e10\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>Time series cover a wide range of real-life phenomena and can be found in many fields.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4a71a96 elementor-widget elementor-widget-text-editor\" data-id=\"4a71a96\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>A time series can be anything from the evolution of a country&#8217;s population or GDP, to an electrocardiogram or the latest Dua Lipa soundtrack.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8841ade elementor-widget elementor-widget-text-editor\" data-id=\"8841ade\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Mathematically, a<strong> time series<\/strong> is a series of <strong>data indexed by time.<\/strong><\/p><p>The analysis and prediction of these series is therefore of prime interest to certain industries or sectors, because in practice, <strong>predicting a time series means predicting the future.<\/strong><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3eceb35 e-flex e-con-boxed e-con\" data-id=\"3eceb35\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-9f7120c e-con-full e-flex e-con\" data-id=\"9f7120c\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-09e7437 elementor-widget elementor-widget-image\" data-id=\"09e7437\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"398\" height=\"260\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/02\/unnamed.png\" class=\"attachment-large size-large wp-image-40593\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/02\/unnamed.png 398w, https:\/\/datascientest.com\/en\/files\/2021\/02\/unnamed-300x196.png 300w\" sizes=\"(max-width: 398px) 100vw, 398px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">The French population and its prediction by the Institut national des sciences d\u00e9mographiques <\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-b4f540c e-flex e-con-boxed e-con\" data-id=\"b4f540c\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-d2ae226 e-con-full e-flex e-con\" data-id=\"d2ae226\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-06cab5f elementor-widget elementor-widget-heading\" data-id=\"06cab5f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How do you break down a time series?\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-cff172e e-flex e-con-boxed e-con\" data-id=\"cff172e\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-caa2f31 e-con-full e-flex e-con\" data-id=\"caa2f31\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-64c1f86 elementor-widget elementor-widget-text-editor\" data-id=\"64c1f86\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Traditionally, a time series is broken down into three elements:<\/p><ul><li>A trend (Tt)<\/li><li>Seasonality (St)<\/li><li>A residual or error (\u03b5t)<\/li><\/ul><p>Mathematically, a time series can be expressed as Xt = Tt + St + \u03b5t<\/p><p>With T the trend, S the seasonality, \u03b5 the residual and t the time index.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ddd716e elementor-widget elementor-widget-heading\" data-id=\"ddd716e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">The trend<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4c5831e elementor-widget elementor-widget-text-editor\" data-id=\"4c5831e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>A trend is the increasing or decreasing behavior of a series over time. It often reflects a phenomenon of growth or decline over the long term.<\/p><p>The trend of a time series can take different forms:<\/p><ul><li>Linear Tt = \u03b1 + \u03b2t<\/li><li>Quadratic Tt = \u03b1 + \u03b2t + \u03b3t\u00b2<\/li><li>Exponential Tt = \u03b1 + \u03b2exp(t)<\/li><\/ul><p>&#8230;.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fac016d elementor-widget elementor-widget-heading\" data-id=\"fac016d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Seasonality<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-452257e elementor-widget elementor-widget-text-editor\" data-id=\"452257e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Seasonality reflects the presence of a periodic phenomenon that repeats itself throughout the time series.<\/p><p>Thus, if the seasonal component repeats itself according to period k. St+k = St<\/p><p>Seasonality is a feature of many data, particularly meteorological data (temperature changes over time).<\/p><p>Some time series show both a trend and a seasonal pattern, as in the case of global air traffic. Air traffic is on the rise, but there is a marked difference between winter and summer traffic.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-c8b6c8c e-flex e-con-boxed e-con\" data-id=\"c8b6c8c\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-5169e65 e-con-full e-flex e-con\" data-id=\"5169e65\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-fedf9c9 elementor-widget elementor-widget-image\" data-id=\"fedf9c9\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"381\" height=\"249\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/02\/unnamed-1.png\" class=\"attachment-large size-large wp-image-40595\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/02\/unnamed-1.png 381w, https:\/\/datascientest.com\/en\/files\/2021\/02\/unnamed-1-300x196.png 300w\" sizes=\"(max-width: 381px) 100vw, 381px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Source: DataScientest <\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-fb57501 e-flex e-con-boxed e-con\" data-id=\"fb57501\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-5dce162 e-con-full e-flex e-con\" data-id=\"5dce162\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-cd2f8be elementor-widget elementor-widget-heading\" data-id=\"cd2f8be\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">The residue<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9156000 elementor-widget elementor-widget-text-editor\" data-id=\"9156000\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The model residual is the part of the time series that cannot be explained by decomposition. A time series cannot be completely decomposed according to trend and seasonality alone.<\/p><p>Ideally, the residual of the model is stationary, i.e. the remaining process does not evolve over time (constant mean and variance). If the residual of our time series is not stationary, this means that certain temporal components are not explained in the model.<\/p><p>Once the trend and seasonality of the time series have been explained, we can then try to explain the decomposition residual using auto-regression or moving-average processes, which gave rise to the <strong>famous ARMA model.<\/strong><\/p><p>The holy grail of time series modeling is to obtain a white noise residual, i.e. one that no longer contains any temporal information. In practice, this means a random, decorrelated stationary signal.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8ba4d1a elementor-widget elementor-widget-heading\" data-id=\"8ba4d1a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Further information<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7ed31ac elementor-widget elementor-widget-text-editor\" data-id=\"7ed31ac\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>&#8211; In addition to seasonality, we sometimes define a cycle that can be considered as a longer-period seasonality, in which case we can define several different cycles.<\/p><p>&#8211; The presence of seasonality between two dates can make comparison between these dates difficult. Depending on the problem, we may therefore seek to correct for seasonal variations in time series. This is the case, for example, with the unemployment rate or growth rate.<\/p><p>&#8211; The decomposition Xt = Tt + St + \u03b5t is said to be additive, but we can also model a time series according to a multiplicative decomposition Xt = Tt (1 + St )(1 + \u03b5t )<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-61be096 e-flex e-con-boxed e-con\" data-id=\"61be096\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-1868be2 e-con-full e-flex e-con\" data-id=\"1868be2\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-b22c4d6 elementor-widget elementor-widget-heading\" data-id=\"b22c4d6\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0b73de0 e-flex e-con-boxed e-con\" data-id=\"0b73de0\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-10b38d2 e-con-full e-flex e-con\" data-id=\"10b38d2\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1a08ba8 elementor-widget elementor-widget-text-editor\" data-id=\"1a08ba8\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In this article, you&#8217;ve learned about the basics of time series: trend, seasonality, residual, white noise and stationarity.<\/p><p>However, beyond time series, data science covers many other issues (<a href=\"https:\/\/datascientest.com\/en\/opencv-everything-you-need-to-know-about-computer-visions-leading-tool\">computer vision<\/a>, natural language processing, data vizualization, etc.) which we invite you to discover by following one of our Data Scientist or <a href=\"https:\/\/datascientest.com\/en\/become-a-data-engineer-the-must-have-skills\">Data Engineer training courses.<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-35ef0f9 e-flex e-con-boxed e-con\" data-id=\"35ef0f9\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-3961fd9 e-con-full e-flex e-con\" data-id=\"3961fd9\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;full&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c58ea8d elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"c58ea8d\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-data-scientist\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">D\u00e9couvrir notre formation en data science<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>New appointment with Daniel, the technical support for DataScientest.&nbsp; the data science expert who accompanies learners throughout their training courses. Today, he talks to us about time series. Time series is one of the most widely studied topics in data science. In this article, you&#8217;ll discover the main components of a time series. What is [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":176894,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Time series: Daniel, can you tell us about them?<\/title>\n<meta name=\"description\" content=\"A time series is a sequence of data points, typically ordered chronologically, that represents the values of a variable or a set of variables over time. 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