Hybrid Approaches of Support Vector Regression and SARIMA Models to Forecast the Inspections Volume

Ruiz-Aguilar, Juan Jesús; Turias, Ignacio J.; Jiménez‐Come, María Jesús; Cerbán, Mar

doi:10.1007/978-3-319-07617-1_44

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…Predictive analytics XGBoost model 55 Machine learning 52 Bayesian network 19,22,23,53,[56][57][58][59] Support vector machine 31,54,60,61 Binary logistic regression 21,62,63 Bayesian belief networks 64 Q-learning algorithm 65 Poisson regression 27 Blockchain 20 Markov chains 64 K-nearest neighbor 65 from ships and inspections are expanded on the basis of possible methods and approaches, it may be possible to concentrate the studies on the prescriptive analytic approach in the future perspectives.…”

Section: Analytic Approach Used Methods On Papersmentioning

confidence: 99%

A bibliometric review on maritime inspection analysis: Current and future insights

Bicen

Çelik

2022

Proceedings of the Institution of Mechanical Engineers, Part M:

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This study conducts a comprehensive bibliometric review of 123 papers, authored by 359 scholars, on maritime inspection analysis published in 77 scientific journals. The bibliographical data are analyzed in R software using the Bibliometrix tool. The most influential publications, journals, authors, and institutions are identified using citation analysis metrics. Three underlying research clusters are identified using the bibliographic coupling methodology: (i) analytic approach-based, (ii) research methodology-based, and (iii) research field-based clusters. Indeed, research collaboration networks are demonstrated at the institution, author, and country levels to address current tendencies in maritime inspection analysis. Besides the compliance solution studies, the future insights of inspection regimes along with green and smart shipping are extensively discussed. The study has great potential to encourage the forthcoming initiatives and research on inspection analytics and digitalization in maritime, offshore, logistics, and relevant fields.

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Section: Analytic Approach Used Methods On Papersmentioning

confidence: 99%

A bibliometric review on maritime inspection analysis: Current and future insights

Bicen

Çelik

2022

Proceedings of the Institution of Mechanical Engineers, Part M:

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“…Moreover, Lee et al (2017) used a SARIMA-SVR model to improve atmospheric pollution forecast accuracy based on the analysis of atmospheric pollution data in the Internet-of-Things (IoT) environment. Finally, Ruiz-Aguilar et al (2014) adopted a SARIMA-SVR model to forecast the inspection volume at the European border, notably the Border Inspection Post of Port of Algeciras Bay. Such works provide support on the capability of the combined linear and nonlinear models, particularly, the SVR and SARIMA models, in improving forecast accuracy.…”

Section: Methodsmentioning

confidence: 99%

“…In the relevant literature, scholars have found the capability of the hybrid seasonal autoregressive integrated moving averages (SARIMA) – support vector regression (SVR) in forecast modeling. Its applicability has achieved fruitful results in relevant domains, as demonstrated by Lee et al (2017), Xu et al (2019) and Ruiz-Aguilar et al (2014), among others.…”

Section: Introductionmentioning

confidence: 99%

Hybrid SVR-SARIMA model for tourism forecasting using PROMETHEE II as a selection methodology: a Philippine scenario

Abellana

Rivero

Aparente

et al. 2020

JTF

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Purpose This paper aims to propose a hybrid-forecasting model for long-term tourism demand forecasting. As such, it attempts to model the tourism demand in the Philippines, which is a relatively underrepresented area in the literature, despite its tourism sector’s growing economic progress. Design/methodology/approach A hybrid support vector regression (SVR) – seasonal autoregressive integrated moving averages (SARIMA) model is proposed to model the seasonal, linear and nonlinear components of the tourism demand in a destination country. The paper further proposes the use of multiple criteria decision-making (MCDM) approaches in selecting the best forecasting model among a set of considered models. As such, a preference ranking organization method for enrichment of evaluations (PROMETHEE) II is used to rank the considered forecasting models. Findings The proposed hybrid SVR-SARIMA model is the best performing model among a set of considered models in this paper using performance criteria that evaluate the errors of magnitude, directionality and trend change, of a forecasting model. Moreover, the use of the MCDM approach is found to be a relevant and prospective approach in selecting the best forecasting model among a set of models. Originality/value The novelty of this paper lies in several aspects. First, this paper pioneers the demonstration of the SVR-SARIMA model’s capability in forecasting long-term tourism demand. Second, this paper is the first to have proposed and demonstrated the use of an MCDM approach for performing model selection in forecasting. Finally, this paper is one of the very few papers to provide lenses on the current status of Philippine tourism demand.

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“…The RPD values are less than 1.4 and greater than two show poor and high performance, respectively. The range of dm is between 0 and 1, where1 is an indicative of high performance of the model (Ruiz-Aguilar et al, 2014;Duveiller et al, 2016).…”

Section: Performance Evaluation Of the Improved Hybrid Modelsmentioning

confidence: 99%

Improving Hybrid Models For Precipitation Forecasting By Combining Nonlinear Machine Learning Methods

Parviz¹,

Rasouli²,

Torabi³

2021

Preprint

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Precipitation forecast, especially on monthly and annual scales, is a key for optimal water resources management and planning, especially in semiarid climates with scarce water. The traditional hybrid models, in which two statistical models are used to separate and simulate linear and nonlinear components of precipitation time series, are still unable to provide accurate precipitation forecasts. This research aims to improve hybrid forecast models by combining one linear model and three nonlinear models with two preprocessing configurations: 1) using residuals of a linear model, representing the nonlinear component with different time steps and 2) using original time series of observations with different time steps, linear model simulations and residuals. Gene Expression Programming (GEP), Support Vector Regression (SVR) and Group Method of Data Handling (GMDH) models were used individually as in the traditional hybrid models and combinedly as in the proposed hybrid models in this study. The performance of the hybrid models was improved by different methods such as inverse variance (Iv) as an error-based method, least square regression, genetic algorithm and SVR. Two weather stations of Tabriz (annual) and Rasht (monthly) in Iran were selected to test the developed models. The results showed that Theil’s coefficient, UII, decreased in configuration one for the Tabriz station by 9% and 15% for SVR and GMDH relative to GEP, suggesting that these two models performed better than GEP in the precipitation forecast. The error criteria used in developing the proposed hybrid models with all forecast combination methods better represent observations than the hybrid model. MSE decreased by 67% and Nash Sutcliffe increased by 5% in the Rasht station in configuration two when we combined the three models using GA to obtain the improved hybrid model relative to the hybrid model combined with SVR. Generally, the hybrid models when SVR, the error based methods and GA were incorporated showed better performance than traditional hybrid models. The developed models have implications for modeling highly nonlinear systems using full advantages of machine learning methods.

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Hybrid Approaches of Support Vector Regression and SARIMA Models to Forecast the Inspections Volume

Cited by 6 publications

References 14 publications

A bibliometric review on maritime inspection analysis: Current and future insights

A bibliometric review on maritime inspection analysis: Current and future insights

Hybrid SVR-SARIMA model for tourism forecasting using PROMETHEE II as a selection methodology: a Philippine scenario

Improving Hybrid Models For Precipitation Forecasting By Combining Nonlinear Machine Learning Methods

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