About: Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models
Creator	Hu, Zhongyi Xiong, Tao Bao, Yukun Ming, Wei Bala, P Ji, P
source	Medline; PMC
abstract	The hybrid ARIMA-SVMs prediction models have been established recently, which take advantage of the unique strength of ARIMA and SVMs models in linear and nonlinear modeling, respectively. Built upon this hybrid ARIMA-SVMs models alike, this study goes further to extend them into the case of multistep-ahead prediction for air passengers traffic with the two most commonly used multistep-ahead prediction strategies, that is, iterated strategy and direct strategy. Additionally, the effectiveness of data preprocessing approaches, such as deseasonalization and detrending, is investigated and proofed along with the two strategies. Real data sets including four selected airlines' monthly series were collected to justify the effectiveness of the proposed approach. Empirical results demonstrate that the direct strategy performs better than iterative one in long term prediction case while iterative one performs better in the case of short term prediction. Furthermore, both deseasonalization and detrending can significantly improve the prediction accuracy for both strategies, indicating the necessity of data preprocessing. As such, this study contributes as a full reference to the planners from air transportation industries on how to tackle multistep-ahead prediction tasks in the implementation of either prediction strategy.
has issue date	2014-02-27(xsd:dateTime)
bibo:doi	10.1155/2014/567246
bibo:pmid	24723814
has license	cc-by
sha1sum (hex)	e80ffcae95f83bc7e4353e746ec75187eb61281e
schema:url	https://doi.org/10.1155/2014/567246
resource representing a document's title	Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models
has PubMed Central identifier	PMC3958729
has PubMed identifier	24723814
schema:publication	ScientificWorldJournal
resource representing a document's body	covid:e80ffcae95f83bc7e4353e746ec75187eb61281e#body_text
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