{"id":130355,"date":"2021-06-07T13:56:52","date_gmt":"2021-06-07T17:56:52","guid":{"rendered":"https:\/\/www.sightline.com\/?p=130355"},"modified":"2026-02-17T15:48:10","modified_gmt":"2026-02-17T20:48:10","slug":"data-forecasting","status":"publish","type":"post","link":"https:\/\/www.sightline.com\/data-forecasting\/blogs\/","title":{"rendered":"Data Forecasting"},"content":{"rendered":"\n<h2>An Industry Brief<\/h2><h2>What is Data Forecasting<\/h2><div class='yarpp yarpp-related yarpp-related-shortcode yarpp-template-list'>\n<!-- YARPP List -->\n<h3>Related posts:<\/h3><ul>\n<li><a href=\"https:\/\/www.sightline.com\/sightline-edm-security-event-detection\/use-cases\/\" rel=\"bookmark\" title=\"Sightline EDM&#8217;s Security Event Detection: Fortifying European Banks for Stringent EU Stress Tests and Regulations\">Sightline EDM&#8217;s Security Event Detection: Fortifying European Banks for Stringent EU Stress Tests and Regulations<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/q-a-unleashing-productivity-in-manufacturing\/blogs\/\" rel=\"bookmark\" title=\"Advanced Analytics in Manufacturing 101: Unleashing Productivity &amp; Profitability\">Advanced Analytics in Manufacturing 101: Unleashing Productivity &amp; Profitability<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/gnsol-partnership\/blogs\/\" rel=\"bookmark\" title=\"Sightline Systems Partners with GNSOL to Offer IT Solutions Abroad\">Sightline Systems Partners with GNSOL to Offer IT Solutions Abroad<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/case-study-federal-defense-capacity-planning-2\/use-cases\/\" rel=\"bookmark\" title=\"Case Study: Discover how Sightline improved federal defense capacity planning through advanced analytics and data-driven strategies.\">Case Study: Discover how Sightline improved federal defense capacity planning through advanced analytics and data-driven strategies.<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/data-analysis-iiot-systems-guide\/blogs\/\" rel=\"bookmark\" title=\"Applying Data Analysis Tools for IIoT Systems\">Applying Data Analysis Tools for IIoT Systems<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/iiot\/blogs\/\" rel=\"bookmark\" title=\"IIoT: Industrial Internet of Things\">IIoT: Industrial Internet of Things<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/what-is-mqtt\/blogs\/\" rel=\"bookmark\" title=\"MQTT Protocol\">MQTT Protocol<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/zero-trust-data-security\/blogs\/\" rel=\"bookmark\" title=\"Zero-Trust Data Security\">Zero-Trust Data Security<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/correlation-data-analysis-engine-brief\/blogs\/\" rel=\"bookmark\" title=\"Data Correlation\">Data Correlation<\/a><\/li>\n<li><a href=\"https:\/\/www.sightline.com\/case-study-global-travel-company-root-cause-analysis\/use-cases\/\" rel=\"bookmark\" title=\"Case Study: Global Travel Company Uses Root Cause Analysis &#038; Predictive Analytics for 20,000 Servers\">Case Study: Global Travel Company Uses Root Cause Analysis &#038; Predictive Analytics for 20,000 Servers<\/a><\/li>\n<\/ul>\n<\/div>\n<h5>Sightline has expertise in collecting, retaining &amp; analyzing time-series data to implement data forecasting.<\/h5>\n<p>&nbsp;<\/p>\n<h2>What is \u201cTime-Series Data\u201d?<\/h2>\n<p>Time series data is a collection of quantities that are assembled over even intervals in time and ordered chronologically.\u00a0 The statistical characteristics of time series data often violate the assumptions of conventional statistical methods. Because of this, analyzing time-series data requires a unique set of tools and methods, collectively known as time series analysis.<\/p>\n<p><strong>Forecasting is one of the ways to analyze time series to find out future trends of the given input data.<\/strong><\/p>\n<h2>There Are Two Classes of Popular Data Forecasting Techniques<\/h2>\n<p><strong>Univariate Time-Series:<\/strong> Only one variable is varying over time. For example, data collected from a sensor measuring the speed of a motor every second. Therefore, each second, you will only have a one-dimensional value, which is the speed.<\/p>\n<p><strong>Multivariate Time-Series:<\/strong> Multiple variables are varying over time. For example, a tri-axial accelerometer. There are three accelerations, one for each axis (x,y,z) and they vary simultaneously over time.<\/p>\n<p>There are multiple forecasting methods or formulas available in each technique. GARCH, SARIMA etc., are examples of Univariate technique. VAR, VECM etc., are examples of multivariate techniques.<\/p>\n<h2>How to Pick The Appropriate Data Forecasting Model<\/h2>\n<p>Apart from univariate vs multivariate techniques, the structure (or) behaviour of data is what influences in picking appropriate \u201cmodel\u201d or \u201cforecast formula\u201d. Few things which impact behaviour of data are: seasonality, trend (linear or nonlinear) etc., in the data.<\/p>\n<p>For example, while using univariate data, SARIMA with appropriate parameters could be a good model to use while dealing with time series data with seasonality. If no seasonality doesn\u2019t exist in the data, ARIMA might be a good approach.<\/p>\n<p><strong>SARIMA:<\/strong> It refers to Seasonal ARIMA.It is basically for <em>series with seasonality component <\/em>in them which hasn\u2019t been handled in any way. Here we need to provide <em>7 parameters<\/em> where 3 for ARIMA(AR, I, MA) and 3 for Seasonal ARIMA(Season AR, Season I, Season MA) and one for seasonality duration(12 months,6 months according to the data)<\/p>\n<p><strong>ARIMA:<\/strong> It is<em> AutoRegression Integrated MovingAverage<\/em>. Here Integrated term refers to differencing that is, for example, calculating 5th term with order 3, it would be 4th + (4th-3rd)+(3rd-2nd) terms.<\/p>\n<p>Sightline EDM analytic capabilities processes the time series data forecasting to help customers in identifying future trends of their machine data by using above described techniques. The advantage with Sightline EDM is, it automatically goes through the data to identify seasonality\/stationarity of data and applies appropriate forecasting methods to identify future trends.<\/p><h2>Related Content:<\/h2>\n","protected":false},"excerpt":{"rendered":"<p>Forecasting uses historical data as inputs to make informed estimates that are predictive in determining the direction of future trends. Choosing appropriate forecasting techniques depends on the type of data being used and behavior of the data. For example, forecasting techniques used for \u201cfinancial data\u201d may not be appropriate for time series data like \u201ccomputer server data\u201d or \u201cdata center network activity data\u201d (though underlying forecasting principles stay the same).<\/p>\n","protected":false},"author":30,"featured_media":225027,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_uf_show_specific_survey":0,"_uf_disable_surveys":false,"footnotes":""},"categories":[4032],"tags":[261,3968],"class_list":["post-130355","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blogs","tag-forecasting","tag-industry-briefs"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v24.5 (Yoast SEO v27.1.1) - 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