Design and implementation of the SARIMA–SVM time series analysis algorithm for the improvement of atmospheric environment forecast accuracy

Lee, Nam-Uk; Shim, Jae-Sung; Ju, Yong-Wan; Park, Seok-Cheon

doi:10.1007/s00500-017-2825-y

Cited by 26 publications

(11 citation statements)

References 12 publications

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“…Likewise, Zou et al (2019) used a combined SARIMA and SVR to develop a predictive model that could forecast the Hand-foot-mouth disease (HFMD) incidence in China. 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.…”

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%

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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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Section: Methodsmentioning

confidence: 99%

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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“…Yao et al proposed a computational framework for integrating wind power uncertainty and carbon tax in economic dispatch (ED) model (Yao et al 2012). Lee et al proposed a seasonal auto regressive integrated moving average-support vector machine (SARIMA-SVM) time series analysis algorithm to improve pollution forecast accuracy (Lee et al 2018).When conducting research based on time series data for CO 2 emission, greyscale prediction is a model that can achieve more accurate predictions with less data, which is quite favored by researchers for carbon emissions prediction. Considering the uncertainty, imperfection, and small sample of CO 2 emissions, we adopted the grey prediction model and optimized it in this study to realize the forecasting.…”

Section: Research On Forecasting Carbon Dioxidementioning

confidence: 99%

Forecasting CO2 Emissions Using a Novel Conformable Fractional Order Discrete Grey Model

Zhu

Xie

Shi

et al. 2021

Preprint

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Accurate and scientific forecasting of carbon dioxide emissions will help make better industrial carbon emission planning so as to promote low-carbon industrial development and achieve sustainable economic growth. For depressing the disturbance of various elements, grey system-based models play an important role in forecasting science. In this paper, we extend the cumulative order from integer order to fractional order based on the discrete gray model, which we call CFDGM (1,1). After introducing the free quantity of the model order, the accuracy of the prevenient grey-based models can be further enhanced. We selected the data for carbon dioxide production by Germany, Japan, and Thailand for modeling. To obtain the optimal order of our grey model, we selected four optimizers to search for the order. The results show that although the search history of the four types of optimizers is different, the search results are the same, which proves that the four types of optimizers are stable and reliable, and the order for which we searched is reliable. By substituting the optimal order into CFDGM (1,1), we obtained the fitting and prediction error of the proposed model. The final results show that a satisfactory fitting effect and forecasting effect is obtained by our proposed model.

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“…This approach is crucial because of the problems encountered in time-series forecasting, where almost all real time series contain linear and nonlinear correlation patterns between the data. Recently, the hybridization of prediction methods has been used with great success to achieve higher prediction accuracy [ 15 , 16 , 19 , 20 , 22 , 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

Improvement of Time Forecasting Models Using Machine Learning for Future Pandemic Applications Based on COVID-19 Data 2020–2022

et al. 2023

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Improving forecasts, particularly the accuracy, efficiency, and precision of time-series forecasts, is becoming critical for authorities to predict, monitor, and prevent the spread of the Coronavirus disease. However, the results obtained from the predictive models are imprecise and inefficient because the dataset contains linear and non-linear patterns, respectively. Linear models such as autoregressive integrated moving average cannot be used effectively to predict complex time series, so nonlinear approaches are better suited for such a purpose. Therefore, to achieve a more accurate and efficient predictive value of COVID-19 that is closer to the true value of COVID-19, a hybrid approach was implemented. Therefore, the objectives of this study are twofold. The first objective is to propose intelligence-based prediction methods to achieve better prediction results called autoregressive integrated moving average–least-squares support vector machine. The second objective is to investigate the performance of these proposed models by comparing them with the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine. Our investigation is based on three COVID-19 real datasets, i.e., daily new cases data, daily new death cases data, and daily new recovered cases data. Then, statistical measures such as mean square error, root mean square error, mean absolute error, and mean absolute percentage error were performed to verify that the proposed models are better than the autoregressive integrated moving average, support vector machine model, least-squares support vector machine, and autoregressive integrated moving average–support vector machine. Empirical results using three recent datasets of known the Coronavirus Disease-19 cases in Malaysia show that the proposed model generates the smallest mean square error, root mean square error, mean absolute error, and mean absolute percentage error values for training and testing datasets compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine models. This means that the predicted value of the proposed model is closer to the true value. These results demonstrate that the proposed model can generate estimates more accurately and efficiently. Compared to the autoregressive integrated moving average, support vector machine, least-squares support vector machine, and autoregressive integrated moving average–support vector machine models, our proposed models perform much better in terms of percent error reduction for both training and testing all datasets. Therefore, the proposed model is possibly the most efficient and effective way to improve prediction for future pandemic performance with a higher level of accuracy and efficiency.

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Design and implementation of the SARIMA–SVM time series analysis algorithm for the improvement of atmospheric environment forecast accuracy

Cited by 26 publications

References 12 publications

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

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

Forecasting CO2 Emissions Using a Novel Conformable Fractional Order Discrete Grey Model

Improvement of Time Forecasting Models Using Machine Learning for Future Pandemic Applications Based on COVID-19 Data 2020–2022

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