{"product_id":"analytical-study-of-air-traffic-using-arfima-time-series-models-9786208444686","title":"Analytical Study of Air Traffic Using ARFIMA Time Series Models","description":"\u003cp\u003e • Author(s): Manohar Dingari\u003cbr\u003e • Publisher: LAP Lambert Academic Publishing\u003cbr\u003e • Publisher Imprint: LAP Lambert Academic Publishing\u003cbr\u003e • BISAC: Probability \u0026amp; Statistics - General\u003c\/p\u003e\u003cp\u003eWhile time series forecasting techniques have been widely developed, the self-similar structure of data has not been adequately addressed. This research focuses on investigating self-similar structures in real-time air traffic data from Air India and Indigo's scheduled domestic flights, aiming to develop a suitable forecasting model for self-similar time series. Self-similarity has proven valuable, particularly in processes like ARFIMA, long-range dependence, and the Hurst parameter. This study explores the current understanding of self-similarity, its concepts, definitions, and applications, offering a roadmap for future research. The book consolidates past works on air traffic modeling using methods such as Box-Jenkins, Exponential Smoothing, and Artificial Neural Networks. It aims to present a comprehensive overview of time series forecasting developments, focusing on air traffic modeling, long-range dependence through self-similarity, and fitting ARFIMA to identify the most effective forecasting model.\u003c\/p\u003e","brand":"Atlantic Books","offers":[{"title":"Paperback","offer_id":46378443571351,"sku":"9786208444686","price":6653.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9786208444686.webp?v=1768763952","url":"https:\/\/atlanticbooks.com\/products\/analytical-study-of-air-traffic-using-arfima-time-series-models-9786208444686","provider":"Atlantic Books","version":"1.0","type":"link"}