SARIMA damp trend grey forecasting model for airline industry

Benítez, Rafael; Nieto, María Rosa

doi:10.1016/j.jairtraman.2019.101736

Cited by 40 publications

(12 citation statements)

References 19 publications

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“…The following detailed solutions were proposed by the present study: (1) Choosing forecasting models with a simple theoretical basis and calculation: This study, considering the theoretical foundation and limited data available, chose the ES, RA, and GM (1, 1) models as the basis for establishing stand-alone models. (2) Constructing a combined forecasting model: The value of combined forecasting models regarding their accuracy has been verified by various studies (Lin et al, 2019;Colino et al, 2012;Jia et al, 2020;Carmona-Benítez & Nieto, 2020;Hu, 2020;Wang et al, 2018). The present study established a combined forecasting model on the basis of TOPSIS, which is an effective method involving simple calculation for multicriteria decision making.…”

Section: Literature Studymentioning

confidence: 55%

Establishing a Combined Forecasting Model: A Case Study on the Logistic Demand of Nanjing’s Green Tea Industry in China

Lin

2020

Technological and Economic Development of Economy

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The sales logistics of tea leaves is a process that organically integrates basic logistics activities, including transportation, storage, loading, unloading, carrying, packaging, distribution processing, delivery, and information processing. This process requires quick and accurate forecasting of the logistics demand in the green tea market and the provision of feedback to businesses and farming partners, revealing the need for a simple and accurate forecasting method. Responding to and solving the unclear information and limited data available regarding the green tea market are critical. Therefore, this study established a simple, quick, and accurate model through the use of time series and the technique for ordering preferences by similarity to the ideal solution. Finally, the actual logistics demand in the Nanjing green tea industry was employed to verify the proposed model’s practicality and feasibility, which may provide a critical reference for relevant parties such as businesses and researchers.

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Section: Literature Studymentioning

confidence: 55%

Establishing a Combined Forecasting Model: A Case Study on the Logistic Demand of Nanjing’s Green Tea Industry in China

Lin

2020

Technological and Economic Development of Economy

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“…Due to uncertain and independent events occurring during the observation time period, time series variable exhibits noise patterns and smoothing methods attempt to segregate data signal and noise from every data point observed during the time period. The dependent structure of events occurring during the observed time period introduced stochastic behavior alongside deterministic behavior [28]. Such stationary time series can be addressed by using autoregressive models and machine learning models [29]- [31].…”

Section: Time Series Analysismentioning

confidence: 99%

Healthcare Operations and Black Swan Event for COVID-19 Pandemic: A Predictive Analytics

Persis

Manimuthu

Sreedharan

2023

IEEE Trans. Eng. Manage.

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COVID-19 pandemic has questioned the way healthcare operations take place globally as the healthcare professionals face an unprecedented task of controlling and treating the COVID-19 infected patients with a highly straining and draining facility due to the erratic admissions of infected patients. However, COVID-19 is considered as a white swan event. Yet, the impact of the COVID-19 pandemic on healthcare operations is highly uncertain and disruptive making it as a black swan event. Therefore, the study explores the impact of the COVID-19 outbreak on healthcare operations and develops machine learning-based forecasting models using time series data to foresee the progression of COVID-19 and further using predictive analytics to better manage healthcare operations. The prediction error of the proposed model is found to be 0.039 for new cases and 0.006 for active COVID-19 cases with respect to mean absolute percentage error. The proposed simulated model further could generate predictive analytics and yielded future recovery rate, resource management ratios, and average cycle time of a patient tested COVID-19 positive. Further, the study will help healthcare professionals to devise better resilience and decision-making for managing uncertainty and disruption in healthcare operations.

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“…The Seasonal Autoregressive Integrated Moving Average (SARIMA) model adds seasonal factors on the basis of the ARIMA model and is suitable for unstable data with trend cycles (Carmona-Benítez and Nieto, 2020). In addition to seasonal changes, this cycle can also be caused by other factors.…”

Section: Arima and Sarimamentioning

confidence: 99%

Runoff forecast and analysis of the probability of dry and wet transition in the Hanjiang River Basin

Jin

Chen

Zhong

2021

Stoch Environ Res Risk Assess

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Runoff prediction has an important guiding role in the planning and management of regional water resources, flood prevention and drought resistance, and can effectively predict the risk of changes in regional water resources. This study used 12 runoff prediction methods to predict the runoff of four hydrological stations in the Hanjiang River Basin (HRB). Through the MCMC method, the HRB runoff probability conversion model from low to high (high to low) is constructed. The study found that the runoff of the HRB had a decreasing trend. In the mid-1980s, the runoff had a significant decreasing trend. The smoother the runoff changes, the easier it is to make accurate prediction. On the whole, the QS-MFM, MFM, MA-MFM, CES and DNN methods have strong generalization ability and can more accurately predict the runoff of the HRB. The Logistic model can accurately simulate the change of runoff status in the HRB. Among them, the HLT station has the fastest conversion rate of drought and flood, and the flow that generates floods is 6 times that of drought. The smaller the basin area, the larger the gap between drought and flood discharge. Overall, this research provides important technical support for the prediction of change in water resources and the transition probability from drought to flood in the HRB.

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SARIMA damp trend grey forecasting model for airline industry

Cited by 40 publications

References 19 publications

Establishing a Combined Forecasting Model: A Case Study on the Logistic Demand of Nanjing’s Green Tea Industry in China

Establishing a Combined Forecasting Model: A Case Study on the Logistic Demand of Nanjing’s Green Tea Industry in China

Healthcare Operations and Black Swan Event for COVID-19 Pandemic: A Predictive Analytics

Runoff forecast and analysis of the probability of dry and wet transition in the Hanjiang River Basin

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