{"id":17,"date":"2024-04-21T15:57:25","date_gmt":"2024-04-21T13:57:25","guid":{"rendered":"https:\/\/airports.website.tuke.sk\/?page_id=17"},"modified":"2024-05-11T22:17:05","modified_gmt":"2024-05-11T20:17:05","slug":"home","status":"publish","type":"page","link":"https:\/\/airports.website.tuke.sk\/","title":{"rendered":"Home"},"content":{"rendered":"<div id='layer_slider_1'  class='avia-layerslider main_color avia-shadow  avia-builder-el-0  el_before_av_section  avia-builder-el-first  container_wrap sidebar_right'  style='height: 451px; max-width: 1280px; margin: 0 auto;'  ><\/div>\n<div id='about'  class='avia-section av-lw2cp92f-b08833453797ef1ecc429d91360e5988 main_color avia-section-default avia-no-border-styling  avia-builder-el-1  el_after_av_layerslider  el_before_av_section  avia-bg-style-scroll container_wrap sidebar_right'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-17'><div class='entry-content-wrapper clearfix'>\n<div  class='flex_column av-4pd8h-929a9d13e6a43c311ee976995bfaf3aa av_one_fourth  avia-builder-el-2  el_before_av_three_fourth  avia-builder-el-first  first flex_column_div  '     ><style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-lvpizhwp-b870ab3d1dde25e4ddc9d6ce807ace93\">\n.avia-image-container.av-lvpizhwp-b870ab3d1dde25e4ddc9d6ce807ace93 img.avia_image{\nbox-shadow:none;\n}\n.avia-image-container.av-lvpizhwp-b870ab3d1dde25e4ddc9d6ce807ace93 .av-image-caption-overlay-center{\ncolor:#ffffff;\n}\n<\/style>\n<div  class='avia-image-container av-lvpizhwp-b870ab3d1dde25e4ddc9d6ce807ace93 av-styling- avia-align-center  avia-builder-el-3  avia-builder-el-no-sibling '   itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><div class=\"avia-image-container-inner\"><div class=\"avia-image-overlay-wrap\"><img decoding=\"async\" fetchpriority=\"high\" class='wp-image-38 avia-img-lazy-loading-not-38 avia_image ' src=\"https:\/\/airports.website.tuke.sk\/wp-content\/uploads\/2024\/05\/mt-185x300.png\" alt='' title='mt'  height=\"300\" width=\"185\"  itemprop=\"thumbnailUrl\" srcset=\"https:\/\/airports.website.tuke.sk\/wp-content\/uploads\/2024\/05\/mt-185x300.png 185w, https:\/\/airports.website.tuke.sk\/wp-content\/uploads\/2024\/05\/mt.png 300w\" sizes=\"(max-width: 185px) 100vw, 185px\" \/><\/div><\/div><\/div><\/div><div  class='flex_column av-429ah-7e1cc352f04121636e889e9512b2f2aa av_three_fourth  avia-builder-el-4  el_after_av_one_fourth  avia-builder-el-last  flex_column_div  '     ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-lvpj1z5b-ce1df9aad350fb52479d386a24139a8c\">\n#top .av-special-heading.av-lvpj1z5b-ce1df9aad350fb52479d386a24139a8c{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-lvpj1z5b-ce1df9aad350fb52479d386a24139a8c .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-lvpj1z5b-ce1df9aad350fb52479d386a24139a8c .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-lvpj1z5b-ce1df9aad350fb52479d386a24139a8c av-special-heading-h3  avia-builder-el-5  el_before_av_textblock  avia-builder-el-first '><h3 class='av-special-heading-tag'  itemprop=\"headline\"  >About me and my work<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><br \/>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-lvpj24p2-86033c19e6cc3f2b5c707e59137494d8\">\n#top .av_textblock_section.av-lvpj24p2-86033c19e6cc3f2b5c707e59137494d8 .avia_textblock{\nfont-size:13px;\n}\n<\/style>\n<section  class='av_textblock_section av-lvpj24p2-86033c19e6cc3f2b5c707e59137494d8 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div class='avia_textblock'  itemprop=\"text\" ><p class=\"has-text-align-justify\">Welcome to the my page. My name is Marek Tomas and I am a doctoral student at Faculty of Aeronautics of Technical university of Kosice in Slovakia. My passion for aviation has been going on since I was little and flying was always fascinated for me. I have never forgotten the feeling when I flew on airplane first time. It was about 30 years ago and I flew on Tunisair Airbus A320. I have never forgotten the moment when the pilot came out of the cockpit and greeted each passenger personally. I fulfilled my dream at the Faculty of Aeronautics and I continue to live it. Aviation is simply my hobby and my passion as well.<\/p>\n<p class=\"has-text-align-justify\">On this page you can find the some results of my doctoral work &#8211; the airport prediction model database. This database was created based on a questionnaire filled out anonymously by airports from all over the world. The questionnaire was part of the dissertation research at the Faculty of Aeronautics of Technical University in Ko\u0161ice in Slovakia.<\/p>\n<p class=\"has-text-align-justify\">The site is intended for airports, which can give a new perspective on the choice of a prediction method. The airports in the database are divided into 3 sizes based on the number of passengers handled and the number of flights. Selection by preference will result in a recommended prediction method used by similar airports.<\/p>\n<\/div><\/section><\/p><\/div>\n\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-164u1-22710d12bab6dc20fcb180dd1fef6e9b\">\n.avia-section.av-164u1-22710d12bab6dc20fcb180dd1fef6e9b{\nbackground-color:#f9f9f9;\nbackground-image:unset;\n}\n<\/style>\n<div id='database'  class='avia-section av-164u1-22710d12bab6dc20fcb180dd1fef6e9b main_color avia-section-default avia-no-shadow  avia-builder-el-7  el_after_av_section  el_before_av_section  avia-bg-style-scroll container_wrap sidebar_right'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-17'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-lw2cm4m3-e5a0e653b49be5d07919b0d806b12bac\">\n#top .av-special-heading.av-lw2cm4m3-e5a0e653b49be5d07919b0d806b12bac{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-lw2cm4m3-e5a0e653b49be5d07919b0d806b12bac .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-lw2cm4m3-e5a0e653b49be5d07919b0d806b12bac .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-lw2cm4m3-e5a0e653b49be5d07919b0d806b12bac av-special-heading-h3 blockquote modern-quote modern-centered  avia-builder-el-8  el_before_av_hr  avia-builder-el-first '><h3 class='av-special-heading-tag'  itemprop=\"headline\"  >Airport prediction methods database<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n<div  class='hr av-lw2cl9gj-39a8739120f3bcd081783a0033f54e67 hr-short  avia-builder-el-9  el_after_av_heading  el_before_av_codeblock  hr-center'><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n\n\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div><div id='descriptions'  class='avia-section av-lvpixmeh-8570c67c45d05b80c1f3143cd78224ab main_color avia-section-default avia-no-shadow  avia-builder-el-11  el_after_av_section  avia-builder-el-last  avia-bg-style-scroll container_wrap sidebar_right'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-17'><div class='entry-content-wrapper clearfix'>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-lvpkay85-21c9ca03ba6f3b2f7d952cf0fbc69eca\">\n#top .av-special-heading.av-lvpkay85-21c9ca03ba6f3b2f7d952cf0fbc69eca{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-lvpkay85-21c9ca03ba6f3b2f7d952cf0fbc69eca .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-lvpkay85-21c9ca03ba6f3b2f7d952cf0fbc69eca .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-lvpkay85-21c9ca03ba6f3b2f7d952cf0fbc69eca av-special-heading-h3 blockquote modern-quote modern-centered  avia-builder-el-12  el_before_av_hr  avia-builder-el-first '><h3 class='av-special-heading-tag'  itemprop=\"headline\"  >Description of airport prediction methods<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n<div  class='hr av-lvr415zg-ea273c021c959fb45baa9dffa5dd5dcb hr-short  avia-builder-el-13  el_after_av_heading  el_before_av_heading  hr-center'><span class='hr-inner '><span class=\"hr-inner-style\"><\/span><\/span><\/div>\n\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-lw2blcnd-47d9f23d66c9685eb5ae181312cf9535\">\n#top .av-special-heading.av-lw2blcnd-47d9f23d66c9685eb5ae181312cf9535{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-lw2blcnd-47d9f23d66c9685eb5ae181312cf9535 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-lw2blcnd-47d9f23d66c9685eb5ae181312cf9535 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-lw2blcnd-47d9f23d66c9685eb5ae181312cf9535 av-special-heading-h3 blockquote modern-quote modern-centered  avia-builder-el-14  el_after_av_hr  el_before_av_one_fourth '><h3 class='av-special-heading-tag'  itemprop=\"headline\"  >ICAO methods<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div>\n<div  class='flex_column av-lw2bg1t9-8f7588a62241a5b1734c51bd77c41209 av_one_fourth  avia-builder-el-15  el_after_av_heading  el_before_av_one_half  first flex_column_div av-hide-on-mobile  '     ><\/div><div  class='flex_column av-56th-80fdd72b79ad8bab0437c1c0a6aab9f6 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.avia-icon-circles-icon-5{\ntransform:translate(108px,-225px);\nleft:28%;\ntop:95%;\n}\n.av-icon-circles-container.av-lvpkdhfz-145aa0717360d283a8285006bea03d98.avia_start_animation .avia-icon-circles-icon-5{\ntransform:translate(0px,0px);\nopacity:1;\ntransition-delay:1.8s;\n}\n.av-icon-circles-container.av-lvpkdhfz-145aa0717360d283a8285006bea03d98 .avia-icon-circles-icon-6{\ntransform:translate(243px,-55px);\nleft:1%;\ntop:61%;\n}\n.av-icon-circles-container.av-lvpkdhfz-145aa0717360d283a8285006bea03d98.avia_start_animation .avia-icon-circles-icon-6{\ntransform:translate(0px,0px);\nopacity:1;\ntransition-delay:2s;\n}\n.av-icon-circles-container.av-lvpkdhfz-145aa0717360d283a8285006bea03d98 .avia-icon-circles-icon-7{\ntransform:translate(195px,155px);\nleft:10%;\ntop:18%;\n}\n.av-icon-circles-container.av-lvpkdhfz-145aa0717360d283a8285006bea03d98.avia_start_animation .avia-icon-circles-icon-7{\ntransform:translate(0px,0px);\nopacity:1;\ntransition-delay:2.2s;\n}\n.av-icon-circles-container.av-lvpkdhfz-145aa0717360d283a8285006bea03d98.avia_start_animation.avia_animation_finished .avia-icon-circles-icon{\ntransition-delay:0s;\n}\n<\/style>\n<div class='av-icon-circles-container av-lvpkdhfz-145aa0717360d283a8285006bea03d98 avia_animate_when_visible  avia-builder-el-17  avia-builder-el-no-sibling ' ><div class='avia-icon-circles-main-logo'  itemprop=\"image\" itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/ImageObject\" ><img src='https:\/\/airports.website.tuke.sk\/wp-content\/uploads\/2024\/05\/ICAO_manual-233x300.png' alt='' title='ICAO_manual'  itemprop=\"thumbnailUrl\"  \/><\/div><div class=\"avia-icon-circles-inner\"><div class='avia-icon-circles-icon av-lvpkd6ew-e2400eaa46997e71d877882a8fa5f903 iconfont av-no-link avia-icon-circles-icon-1' data-icon-circles-icon=\"1\" aria-hidden='true' data-av_icon='\ue8b7' data-av_iconfont='entypo-fontello'><\/div><div class='avia-icon-circles-icon-text av-lvpkd6ew-e2400eaa46997e71d877882a8fa5f903 avia-icon-circles-icon-text-1' data-icon-circles-text=\"1\"><div class=\"avia-icon-circles-icon-text-inner\"><div class=\"icon-title\">Decomposition methods<\/div><div class=\"icon-description\"><p>Decomposition methods involve the dissection of the problem into various components. These methods are particularly relevant when strong seasonality or cyclical patterns exist in the historical data. These methods can be used to identify three aspects of the underlying pattern of the data: the trend factor, the seasonal factor and any cyclical factor that may exist.<\/p>\n<\/div><\/div><\/div><div class='avia-icon-circles-icon av-2s5st-06f959f853b9701964e999599c9d5a22 iconfont av-no-link avia-icon-circles-icon-2' data-icon-circles-icon=\"2\" aria-hidden='true' data-av_icon='\ue8b6' data-av_iconfont='entypo-fontello'><\/div><div class='avia-icon-circles-icon-text av-2s5st-06f959f853b9701964e999599c9d5a22 avia-icon-circles-icon-text-2' data-icon-circles-text=\"2\"><div class=\"avia-icon-circles-icon-text-inner\"><div class=\"icon-title\">The time-series analysis methods <\/div><div class=\"icon-description\"><p>The time-series analysis methods are largely based on the assumption that historical patterns will continue, and they rely heavily on the availability of historical data. A first step when forecasting air traffic activity is usually to study the historical data (time series) and determine the trend in traffic development. In the context of medium-term or long-term forecasting, a traffic trend represents the development in traffic over many years, isolated from short-term fluctuations in traffic levels. When deriving a medium-term or long-term forecast by extrapolating from the traffic trend, the forecaster assumes that the factors which determined the historical development of the traffic will continue to operate in the future as in the past, except that their impact may change gradually, and steady-state conditions will continue into the future.<\/p>\n<\/div><\/div><\/div><div class='avia-icon-circles-icon av-1zaal-60b24cd968c05d08daf5e2571de88c5d iconfont av-no-link avia-icon-circles-icon-3' data-icon-circles-icon=\"3\" aria-hidden='true' data-av_icon='\ue8b8' data-av_iconfont='entypo-fontello'><\/div><div class='avia-icon-circles-icon-text av-1zaal-60b24cd968c05d08daf5e2571de88c5d avia-icon-circles-icon-text-3' data-icon-circles-text=\"3\"><div class=\"avia-icon-circles-icon-text-inner\"><div class=\"icon-title\">ARIMA<\/div><div class=\"icon-description\"><p>ARIMA, standing for Autoregressive Integrated Moving Average, is a versatile model for analyzing and forecasting time series data. It decomposes the data into three key components:<br \/>\n1. Autoregression (AR) - this component captures the influence of a series\u2019 past values on its future values. In simpler terms, AR considers how past observations (lags) affect the current value. It\u2019s denoted as AR(p), where \u2018p\u2019 represents the number of lagged observations included in the model.<br \/>\n2. Differencing (I) - stationarity is a crucial assumption for many time series analyses. Differencing involves subtracting a previous value from the current value, often required to achieve stationarity.<br \/>\n3. Moving Average (MA) - this component accounts for the effect of past forecast errors (residuals) on the current prediction. It considers the average of past errors (lags) to improve the forecast accuracy. MA is denoted by MA(q), where \u2018q\u2019 represents the number of lagged errors incorporated in the model.<\/p>\n<\/div><\/div><\/div><div class='avia-icon-circles-icon av-lvr2mcne-ebc5a7b3ff737aaa1e39ec1e5b7f1ed1 iconfont av-no-link avia-icon-circles-icon-4' data-icon-circles-icon=\"4\" aria-hidden='true' data-av_icon='\ue8c6' data-av_iconfont='entypo-fontello'><\/div><div class='avia-icon-circles-icon-text av-lvr2mcne-ebc5a7b3ff737aaa1e39ec1e5b7f1ed1 avia-icon-circles-icon-text-4' data-icon-circles-text=\"4\"><div class=\"avia-icon-circles-icon-text-inner\"><div class=\"icon-title\">SARIMA<\/div><div class=\"icon-description\"><p>SARIMA (Seasonal ARIMA) builds upon ARIMA\u2019s strengths by incorporating an additional dimension: seasonality. This is particularly beneficial for data exhibiting recurring patterns at fixed intervals, such as monthly sales data with holiday spikes. Here\u2019s how SARIMA tackles seasonality:<br \/>\n1. Seasonal Autoregression (SAR)  - similar to AR, SAR considers the influence of past seasonal values on the current value. It captures the impact of past seasonal patterns on future forecasts.<br \/>\n2. Seasonal Differencing (SI) - analogous to differencing, seasonal differencing focuses on removing seasonal patterns from the data to achieve stationarity.<br \/>\n3. Seasonal Moving Average (SMA) - this component incorporates the influence of past seasonal forecast errors into the current prediction, similar to the moving average component in ARIMA.<\/p>\n<\/div><\/div><\/div><div class='avia-icon-circles-icon av-lvr2mp95-866c448fd9574d62ebf8420cc4954e1e iconfont av-no-link avia-icon-circles-icon-5' data-icon-circles-icon=\"5\" aria-hidden='true' data-av_icon='\ue84b' data-av_iconfont='entypo-fontello'><\/div><div class='avia-icon-circles-icon-text av-lvr2mp95-866c448fd9574d62ebf8420cc4954e1e avia-icon-circles-icon-text-5' data-icon-circles-text=\"5\"><div class=\"avia-icon-circles-icon-text-inner\"><div class=\"icon-title\">Casual methods <\/div><div class=\"icon-description\"><p>Casual methods extensive use has been made of trend forecasting by basing judgement on past growth trends, which the analyst simply extrapolates, based on the historical values. In the short term, this approach appears to be reliable, especially when the extrapolation procedure is applied with modified growth rates to account for short-term disturbance in underlying trends. In the long term, this type of extrapolation is likely to be unreliable and is theoretically difficult to substantiate. Consequently, forecasts derived by taking into account how economic, social and operational conditions affect the development of traffic offer an alternative to time-series analysis.<\/p>\n<\/div><\/div><\/div><div class='avia-icon-circles-icon av-lvr2n0qx-f5fab4422dc9a766fb64f5794f1e83ba iconfont av-no-link avia-icon-circles-icon-6' data-icon-circles-icon=\"6\" aria-hidden='true' data-av_icon='\ue8c5' data-av_iconfont='entypo-fontello'><\/div><div class='avia-icon-circles-icon-text av-lvr2n0qx-f5fab4422dc9a766fb64f5794f1e83ba avia-icon-circles-icon-text-6' data-icon-circles-text=\"6\"><div class=\"avia-icon-circles-icon-text-inner\"><div class=\"icon-title\">The Delphi technique <\/div><div class=\"icon-description\"><p>The Delphi technique is a special procedure for forecasting by consolidation of opinions on the future. It has two steps. A selected group of qualified people are first presented with a questionnaire in which they are requested to indicate a most probable course of development in the activity being forecast. The initial returns are then consolidated and the composite response returned to all contributors giving them the opportunity to revise their original assessments in light of prevailing opinions among other experts. The Delphi technique is a practical means of bringing together information from many experts and moving towards a consensus among them.<\/p>\n<\/div><\/div><\/div><div class='avia-icon-circles-icon av-lvr2nd7g-5747f0abdc1d081153e66225cc227af4 iconfont av-no-link avia-icon-circles-icon-7' data-icon-circles-icon=\"7\" aria-hidden='true' data-av_icon='\ue8b4' data-av_iconfont='entypo-fontello'><\/div><div class='avia-icon-circles-icon-text av-lvr2nd7g-5747f0abdc1d081153e66225cc227af4 avia-icon-circles-icon-text-7' data-icon-circles-text=\"7\"><div class=\"avia-icon-circles-icon-text-inner\"><div class=\"icon-title\">Autocorrelation method<\/div><div class=\"icon-description\"><p>Autocorrelation method, sometimes known as serial correlation in the discrete time case, is the correlation of a signal with a delayed copy of itself as a function of delay. Informally, it is the similarity between observations of a random variable as a function of the time lag between them. The analysis of autocorrelation is a mathematical tool for finding repeating patterns, such as the presence of a periodic signal obscured by noise, or identifying the missing fundamental frequency in a signal implied by its harmonic frequencies. It is often used in signal processing for analyzing functions or series of values, such as time domain signals.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><div  class='flex_column av-lw2bggm0-5d4097768c567f718ad3dbb59ec561ae av_one_fourth  avia-builder-el-18  el_after_av_one_half  el_before_av_one_full  flex_column_div av-hide-on-mobile  '     ><\/div><div  class='flex_column av-pycl-37adcbb5c899e841ff67030ed991b808 av_one_full  avia-builder-el-19  el_after_av_one_fourth  avia-builder-el-last  first flex_column_div  column-top-margin'     ><p>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-lvr3zy3i-631f800e870a63c94de7abb499b388c4\">\n#top .av-special-heading.av-lvr3zy3i-631f800e870a63c94de7abb499b388c4{\npadding-bottom:10px;\n}\nbody .av-special-heading.av-lvr3zy3i-631f800e870a63c94de7abb499b388c4 .av-special-heading-tag .heading-char{\nfont-size:25px;\n}\n.av-special-heading.av-lvr3zy3i-631f800e870a63c94de7abb499b388c4 .av-subheading{\nfont-size:15px;\n}\n<\/style>\n<div  class='av-special-heading av-lvr3zy3i-631f800e870a63c94de7abb499b388c4 av-special-heading-h3 blockquote modern-quote modern-centered  avia-builder-el-20  el_before_av_textblock  avia-builder-el-first '><h3 class='av-special-heading-tag'  itemprop=\"headline\"  >Non ICAO methods<\/h3><div class=\"special-heading-border\"><div class=\"special-heading-inner-border\"><\/div><\/div><\/div><br \/>\n<section  class='av_textblock_section av-lvr3wg74-b4ea9acf7d4cc056325607aacf280fbd '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/CreativeWork\" ><div class='avia_textblock'  itemprop=\"text\" ><p class=\"has-text-align-justify\"><strong>A Grey Markov Model (GMM<\/strong>) is a hybrid model that combines the Grey System and Markov Chain theories. The Grey System theory is used to predict trend values, while the Markov Chain theory is used to forecast fluctuation values. This combination allows the model to provide forecast results that involve two aspects of information.The Grey prediction model, often expressed as GM, where u is the order of the differential equation and time series data.<\/p>\n<p class=\"has-text-align-justify\"><strong>The Global Vector Autoregressive (GVAR)<\/strong> approach, provides a relatively simple yet effective way of modelling interactions in a complex high-dimensional system such as the global economy. Although GVAR is not the first large global macroeconomic model of the world economy, its methodological contributions lay in dealing with the curse of dimensionality.<\/p>\n<p class=\"has-text-align-justify\"><strong>AMORPH <\/strong>utilizes a new Bayesian statistical approach to interpreting X-ray diffraction results of samples with both crystalline and amorphous components. The program simulates background patterns previously applied manually, providing reproducible results, and significantly reducing inter- and intra-user biases.<\/p>\n<p class=\"has-text-align-justify\"><strong>AIRHART <\/strong>is a modular total airport management platform that enables airports and all stakeholders to become smarter &#8211; together. AIRHART facilitates an effective eco-system for optimizing operations, passenger experience, commercial excellence and the ability to adapt to new needs &#8211; faster.<\/p>\n<p class=\"has-text-align-justify\"><strong>PLS-PM\u00a0<\/strong>a co mponent-based estimation approach that differs from the covariance-based structural equation modeling. The measurement models represent the relationships between the observed data and the latent variables. The structural model represents the relationships between the latent variables.<\/p>\n<p class=\"has-text-align-justify\"><strong>A Petri net<\/strong> is one of several mathematical modeling languages for the description of distributed systems. It is a class of discrete event dynamic system. A Petri net is a directed bipartite graph that has two types of elements: places and transitions. Place elements are depicted as white circles and transition elements are depicted as rectangles. A place can contain any number of tokens, depicted as black circles.<\/p>\n<\/div><\/section><\/p><\/div>\n<\/div><\/div><\/div><!-- close content main div --><\/div><\/div><div id='after_section_3'  class='main_color av_default_container_wrap container_wrap sidebar_right'  ><div class='container av-section-cont-open' ><div class='template-page content  av-content-small alpha units'><div class='post-entry post-entry-type-page post-entry-17'><div class='entry-content-wrapper clearfix'>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-17","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/airports.website.tuke.sk\/index.php?rest_route=\/wp\/v2\/pages\/17","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/airports.website.tuke.sk\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/airports.website.tuke.sk\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/airports.website.tuke.sk\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/airports.website.tuke.sk\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=17"}],"version-history":[{"count":101,"href":"https:\/\/airports.website.tuke.sk\/index.php?rest_route=\/wp\/v2\/pages\/17\/revisions"}],"predecessor-version":[{"id":170,"href":"https:\/\/airports.website.tuke.sk\/index.php?rest_route=\/wp\/v2\/pages\/17\/revisions\/170"}],"wp:attachment":[{"href":"https:\/\/airports.website.tuke.sk\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=17"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}