{"id":180490,"date":"2024-03-20T23:27:16","date_gmt":"2024-03-20T22:27:16","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=180490"},"modified":"2024-02-27T17:38:39","modified_gmt":"2024-02-27T16:38:39","slug":"facebook-prophet-all-you-need-to-know","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/facebook-prophet-all-you-need-to-know","title":{"rendered":"Facebook Prophet : All you need to know"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"180490\" class=\"elementor elementor-180490\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b051ec0 e-flex e-con-boxed e-con\" data-id=\"b051ec0\" 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\t\t<div class=\"elementor-element elementor-element-dbf80b4 elementor-widget elementor-widget-heading\" data-id=\"dbf80b4\" 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\">Facebook Prophet is an open-source Python library offering an intuitive, automated approach to capturing trends, seasons and exceptional events in time series. Discover why this Machine Learning-based tool has revolutionized predictive data analysis!<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6f6a93d elementor-widget elementor-widget-text-editor\" data-id=\"6f6a93d\" 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>One of the great recent technological advances is the ability to predict future trends from historical data.<\/p><p>This is one of the main <a href=\"https:\/\/datascientest.com\/en\/data-science-bootcamp-definition-training-benefits\">benefits of Data Science,<\/a> and its applications are numerous. It can be used to forecast the weather, or the performance of a <a href=\"https:\/\/datascientest.com\/en\/open-banking-data-science\">stock on the stock market.<\/a><\/p><p>It&#8217;s also a precious asset in e-commerce, for anticipating fashions, and in healthcare, for diagnosing serious illnesses at an early stage.<\/p><p>To accomplish these feats, the secret is time-series forecasting. However, this is a complex process even for the greatest statistical experts. To simplify the task, Meta has created the Facebook Prophet tool.<\/p>\t\t\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-d4bf159 e-flex e-con-boxed e-con\" data-id=\"d4bf159\" 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\t\t<div class=\"elementor-element elementor-element-98d34ba elementor-widget elementor-widget-heading\" data-id=\"98d34ba\" 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?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-856a0f9 elementor-widget elementor-widget-text-editor\" data-id=\"856a0f9\" 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 <a href=\"https:\/\/datascientest.com\/en\/time-series-daniel-can-you-tell-us-about-them\">time series is a collection of chronologically ordered data.<\/a> Within such a dataset, each <a href=\"https:\/\/datascientest.com\/en\/time-series-plot-python-the-tutorial\">observation corresponds to a specific moment in time<\/a>, in the form of a continuous temporal sequence, often characterized by fluctuations such as trends or seasons.<\/p><p>Trends represent long-term evolution. They can be upward, downward or stable, and can be influenced by economic, demographic or environmental factors.<\/p><p>Seasons, on the other hand, are regular or periodic variations that recur at fixed intervals. For example, during the Christmas season, gift sales increase sharply. This is annual seasonality.<\/p><p><a href=\"https:\/\/datascientest.com\/en\/sequences-and-series-understanding-these-two-mathematical-concepts\">This data is used to forecast sales<\/a>, weather or financial performance. However, modeling them is a tricky business, due to a number of specific characteristics.<\/p><p>For example, &#8220;noise&#8221; refers to the random and unpredictable fluctuations present in time series. This can be due to external factors or measurement errors.<\/p><p>In the past, time series forecasting relied on traditional methods such as ARIMA (AutoRegressive Integrated Moving Average) models. Their implementation was difficult and required statistical expertise. In 2017, Facebook changed all that.<\/p>\t\t\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-c101e57 e-flex e-con-boxed e-con\" data-id=\"c101e57\" 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\t\t<div class=\"elementor-element elementor-element-3d185f5 elementor-widget elementor-widget-heading\" data-id=\"3d185f5\" 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 Facebook Prophet?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9456641 elementor-widget elementor-widget-text-editor\" data-id=\"9456641\" 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>Developed by Sean J. Taylor and Ben Letham, <strong>Facebook Prophet<\/strong> is an <a href=\"https:\/\/datascientest.com\/en\/open-source-definition-use-cases-and-more\">open-source library<\/a> designed to provide an accessible, high-performance solution for time-series forecasting.<\/p><p>It is particularly aimed at users without statistical expertise, thanks to its automation features. Its approach is based on the marriage of traditional methods and modern Machine Learning techniques.<\/p><p><a href=\"https:\/\/datascientest.com\/en\/the-var-model-from-business-approach-to-results-plotting\">Unlike ARIMA models,<\/a> which can be difficult to parameterize and don&#8217;t always handle complex seasons well, Prophet is far simpler and more powerful.<\/p><p>It is based on an additive decomposition model, separating the time series into three main components: trend, seasonality and vacations (i.e. exceptional events).<\/p><p>The presence of a trend component enables it to handle long-term evolutions, while the seasonality model helps capture periodic variations. What&#8217;s more, public holidays and other events are automatically integrated to improve prediction accuracy!<\/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-10d6c17 elementor-widget elementor-widget-image\" data-id=\"10d6c17\" 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\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/07\/facebook-prophet-datascientest1.jpg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/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 class=\"elementor-element elementor-element-6dbe85d elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"6dbe85d\" 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\/en\/data-courses\">\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\">Learn how to use Facebook Prophet<\/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\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d5ef673 e-flex e-con-boxed e-con\" data-id=\"d5ef673\" 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\t\t<div class=\"elementor-element elementor-element-400871e elementor-widget elementor-widget-heading\" data-id=\"400871e\" 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 does it work?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-08b2b5f elementor-widget elementor-widget-text-editor\" data-id=\"08b2b5f\" 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><strong>Facebook Prophet<\/strong> works in three main stages. First, the pre-processing phase consists of cleaning and preparing the data.<\/p><p>The tool automatically handles missing values, anomalies and outliers. Users can therefore remain focused on understanding the time series, rather than performing these tasks manually.<\/p><p>The modeling then takes into account trend and seasonality to estimate the components associated with each time observation.<\/p><p>Each season can be daily, weekly, monthly, annual or customized according to context. After this estimation, Prophet forecasts for the desired future period.<\/p>\t\t\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-5773986 e-flex e-con-boxed e-con\" data-id=\"5773986\" 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\t\t<div class=\"elementor-element elementor-element-e0a7f0a elementor-widget elementor-widget-heading\" data-id=\"e0a7f0a\" 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\">Advantages and disadvantages<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-59f1759 elementor-widget elementor-widget-text-editor\" data-id=\"59f1759\" 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>One of the main strengths of <strong>Facebook Prophet<\/strong> is, of course, its ease of use. Even non-technical users can easily exploit it to make quick and effective predictions.<\/p><p>This tool therefore <strong>democratizes time series modeling for a wider audience<\/strong>. What&#8217;s more, the automatic handling of exceptional events is a valuable asset.<\/p><p>This allows seasonal variations to be taken into account, improving the accuracy of predictions during special periods.<\/p><p>And for complex time series with multiple levels of seasonality, Prophet is ideal for managing multiple seasons. This makes it a versatile solution for a wide variety of applications.<\/p><p>Nevertheless, it&#8217;s important to be aware of the limitations of this solution. Like any statistical model, it can be influenced by outliers that distort predictions.<\/p><p><a href=\"https:\/\/datascientest.com\/en\/preprocessing-what-is-it-how-does-it-work\">This is why data pre-processing<\/a> is essential to minimize this effect. In addition, the tool may have difficulty in capturing the more complex, non-linear dependencies between variables&#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-f99e008 elementor-widget elementor-widget-image\" data-id=\"f99e008\" 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\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/07\/facebook-prophet-datascientest2.jpg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/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 class=\"elementor-element elementor-element-9880107 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"9880107\" 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\/en\/data-scientist-course\">\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\">Mastering Facebook Prophet<\/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\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6b9eb18 e-flex e-con-boxed e-con\" data-id=\"6b9eb18\" 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\t\t<div class=\"elementor-element elementor-element-7faf9f8 elementor-widget elementor-widget-heading\" data-id=\"7faf9f8\" 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 are the fields of application?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d339a0d elementor-widget elementor-widget-text-editor\" data-id=\"d339a0d\" 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>Since its launch, <strong>Facebook Prophet<\/strong> has made a name for itself in the e-commerce industry. Based on past sales data, seasonal trends and special events such as promotions or sales, this <a href=\"https:\/\/datascientest.com\/en\/gensim-the-python-library-for-topic-modelling\">Python library can provide accurate forecasts.<\/a><\/p><p>This enables companies to better manage their inventories, plan their <a href=\"https:\/\/datascientest.com\/en\/digital-marketing-definition-professions-training\">marketing campaigns<\/a> and anticipate peaks in activity.<\/p><p>Similarly, in the financial sector, Prophet has changed everything by making it possible to analyze historical revenue and profit data to identify trends and seasons.<\/p><p>With this crucial performance forecasting information, decision-makers are able to anticipate periods of growth and slowdown for budget planning and investment choices.<\/p><p><strong>Investors and other financial institutions<\/strong> can also use it to predict fluctuations in share prices, exchange rates or commodity prices. In this way, they can minimize risk.<\/p><p>In the healthcare sector, the tool can be used to <strong>predict time series of medical data<\/strong>. This includes, for example, hospital admissions, medical consultations and infection rates.<\/p><p>As a result, hospitals can better plan their resources, anticipate peak activity periods, and ultimately provide better patient care.<\/p>\t\t\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-48c165f e-flex e-con-boxed e-con\" data-id=\"48c165f\" 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\t\t<div class=\"elementor-element elementor-element-91c0844 elementor-widget elementor-widget-heading\" data-id=\"91c0844\" 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: Facebook Prophet, time series forecasting accessible to all<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cc074ce elementor-widget elementor-widget-text-editor\" data-id=\"cc074ce\" 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>Thanks to its extreme simplicity,<strong> Facebook Prophet<\/strong> has enabled a wide audience to exploit time series forecasting.<\/p><p>The tool is used by <strong>Data Science experts<\/strong> and business users alike, offering them a myriad of new possibilities.<\/p><p>As an open-source library, it continues to be improved over time by researchers and developers. We can therefore expect it to continue to occupy an essential place in the field of predictive analytics.<\/p><p>To learn how to master Prophet and all the best Data Science tools, you can choose DataScientest. Our distance learning courses give you all the skills you need to become a Data Analyst, Data Scientist, Data Engineer or ML Engineer.<\/p><p>In the various modules, you&#8217;ll discover the Python language and its various libraries, such as Prophet, NumPy and Pandas. You&#8217;ll also learn how to handle Machine Learning techniques and Scikit-learn tools.<\/p><p>All our courses are entirely distance learning, and lead to both a <strong>state-recognized diploma and certification from our cloud partners AWS and Microsoft Azure<\/strong>. Don&#8217;t wait any longer and discover DataScientest!<\/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-ea11776 elementor-widget elementor-widget-image\" data-id=\"ea11776\" 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\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/07\/facebook-prophet-datascientest3.jpg\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/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 class=\"elementor-element elementor-element-e73a458 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"e73a458\" 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\/en\/data-courses\">\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\">Starting a Data training course<\/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 class=\"elementor-element elementor-element-572550b elementor-widget elementor-widget-text-editor\" data-id=\"572550b\" 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>Now you know all about Facebook Prophet. For more information on the same subject, take a look at our <a href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-all-about-the-job\">Machine Learning dossier!<\/a><\/p>\t\t\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-24664c3 e-flex e-con-boxed e-con\" data-id=\"24664c3\" 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\t\t<div class=\"elementor-element elementor-element-29b4655 elementor-widget elementor-widget-html\" data-id=\"29b4655\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"What is a time series?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"The term time series refers to a set of chronologically ordered data. Within such a dataset, each observation corresponds to a specific moment in time, and takes the form of a continuous temporal sequence, often characterized by fluctuations such as trends or seasons.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"What is Facebook Prophet?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Developed by Sean J. Taylor and Ben Letham, Facebook Prophet is an open-source library designed to provide an accessible, high-performance solution for time-series forecasting. It is particularly aimed at users without statistical expertise, thanks to its automation features. Its approach is based on the marriage of traditional methods and modern Machine Learning techniques.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"How does it work?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Facebook Prophet works in three main stages. First, the pre-processing phase consists of cleaning and preparing the data. The tool automatically handles missing values, anomalies and outliers. Users can therefore remain focused on understanding the time series, rather than performing these tasks manually.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"What are the scopes of application?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Since its launch, Facebook Prophet has made a name for itself in the e-commerce industry. Based on past sales data, seasonal trends and special events such as promotions or sales, this Python library can provide accurate forecasts.\"\n    }\n  }]\n}\n<\/script>\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>Facebook Prophet is an open-source Python library offering an intuitive, automated approach to capturing trends, seasons and exceptional events in time series. Discover why this Machine Learning-based tool has revolutionized predictive data analysis! One of the great recent technological advances is the ability to predict future trends from historical data. This is one of the [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":180492,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2423],"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>Facebook Prophet : All you need to know<\/title>\n<meta name=\"description\" content=\"Explore everything you need to know about Facebook Prophet, an open-source forecasting tool. 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