Citation map · Study

SZodixStudyAcademic Library

Shelves Open access

Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated

Damp trend Grey Model forecasting method for airline industry

8 references·9 citing·41 second hop

FromToDepth

this work references cited by second hop

References

2008Forecasting with Exponential Smoothing : The State Space Approach818 cited
2009Grey system theory-based models in time series prediction788 cited
1985Forecasting Trends in Time Series399 cited
2006Findings from evidence-based forecasting: Methods for reducing forecast error199 cited
2001Long lead-time forecasting of UK air passengers by Holt–Winters methods with damped trend106 cited
2009Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures91 cited
1989Note—Seasonal Exponential Smoothing with Damped Trends82 cited
2003Determining flight frequencies on an airline network with demand–supply interactions73 cited

Cited by

2015On the new model system and framework of grey system theory63 cited
2020Forecasting manufacturing industrial natural gas consumption of China using a novel time-delayed fractional grey model with multiple fractional order58 cited
2022An improved grey multivariable time-delay prediction model with application to the value of high-tech industry53 cited
2022Advance in grey system theory and applications in science and engineering52 cited
2019SARIMA damp trend grey forecasting model for airline industry51 cited
2022Forecasting the economic indices of the high-tech industries in China using the grey multivariable convolution model

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

40 cited

2022Online public opinion prediction based on rolling fractional grey model with new information priority40 cited

2014Regularized multivariable grey model for stable grey coefficients estimation37 cited

2020Improving Grey Prediction Model and Its Application in Predicting the Number of Users of a Public Road Transportation System28 cited