Damp trend Grey Model forecasting method for airline industry

Benítez, Rafael; Paredes, Rafael Bernardo Carmona; Lodewijks, Gabriël; Nabais, João Lemos

doi:10.1016/j.eswa.2013.02.014

Cited by 57 publications

(11 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study also revealed that the rule coincided with the Shapley value of the game associated with the problem. Furthermore, there is some important research in the literature that used grey system models to design network airline routes, determine flight frequencies on individual routes, and forecast airline passenger demand (e.g., Hsu and Wen 2000 ; Benitez et al 2013 ). Table 1 presents a summary of the literature regarding different demand and problem types, and the solution methods, and compares the problems existing in the literature with the problem suggested in this research.…”

Section: Related Workmentioning

confidence: 99%

Collaborative airline revenue sharing game with grey demand data

Olgun

2021

Cent Eur J Oper Res

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Collaborative airline revenue sharing game with grey demand data

Olgun

2021

Cent Eur J Oper Res

View full text Add to dashboard Cite

“…In their study, Bezuglov et al considered the short-term traffic prediction along freeways using grey models [24]. Predicting the growth of demand for trip in the aerial industry [25], predicting the return flow of the end-of-life vehicles (ELVs) [26], and predicting short-term traffic in urban roads [27] are examples of other applications of the grey prediction models in the transportation industry.…”

Section: Introductionmentioning

confidence: 99%

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

Balochian

Baloochian

2020

Journal of Intelligent Systems

View full text Add to dashboard Cite

AbstractThe recent increase in the road transportation necessitates scheduling to reduce the adverse impacts of the road transportation and evaluate the effectiveness of previous actions taken in this context. However, it is impossible to undertake the scheduling and evaluation tasks unless previous information are available to predict the future. The grey model requires a limited volume of data for estimating the behavior of an unknown system. It provides high-accuracy predictions based on few data points. Various grey prediction models have been proposed so far, in which three different approaches are followed to increase the accuracy: (1) data preprocessing, (2) improved equation models, and (3) error improvement or error balancing. In this paper, firstly, a theorem is proposed and proved to recognize the parameters affecting two grey models, namely GM(1, 1) and FGM(1, 1). Then, the effective parameters are adjusted through particle swarm optimization (PSO) to formulate two adjusted models, namely IGM(1, 1) and IFGM(1, 1). According to the simulation results of the proposed models, accuracy of the modeling improved by a minimum of 14.24% and a maximum of 82.39%. Finally, the number of users of a public road transportation system was predicted using the proposed models. The results showed enhanced accuracy (by 7.7%) of the proposed models for predicting the number of users of the public road transportation system.

show abstract

“…The GM(1,1) is the main grey theory forecasting model with good short-term forecasting accuracy. Due to the few samples required and its fast calculations, it is successfully used in engineering, technology, industrial and agricultural production, economics, and many other fields [29][30][31][32][33][34]. However, for practical GM(1,1) applications, the forecasting accuracy may decrease when the original data show an increasing trend [35] or when the data samples rapidly mutate [13].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Forecasting Approach Based on Grey Theory and Cuckoo Search Algorithm: A Case Study for Electricity Consumption in New South Wales

Jiang

Zhou

Jiang

et al. 2014

Abstract and Applied Analysis

View full text Add to dashboard Cite

With rapid economic growth, electricity demand is clearly increasing. It is difficult to store electricity for future use; thus, the electricity demand forecast, especially the electricity consumption forecast, is crucial for planning and operating a power system. Due to various unstable factors, it is challenging to forecast electricity consumption. Therefore, it is necessary to establish new models for accurate forecasts. This study proposes a hybrid model, which includes data selection, an abnormality analysis, a feasibility test, and an optimized grey model to forecast electricity consumption. First, the original electricity consumption data are selected to construct different schemes (Scheme 1: short-term selection and Scheme 2: long-term selection); next, the iterative algorithm (IA) and cuckoo search algorithm (CS) are employed to select the best parameter of GM(1,1). The forecasted day is then divided into several smooth parts because the grey model is highly accurate in the smooth rise and drop phases; thus, the best scheme for each part is determined using the grey correlation coefficient. Finally, the experimental results indicate that the GM(1,1) optimized using CS has the highest forecasting accuracy compared with the GM(1,1) and the GM(1,1) optimized using the IA and the autoregressive integrated moving average (ARIMA) model.

show abstract

Damp trend Grey Model forecasting method for airline industry

Cited by 57 publications

References 10 publications

Collaborative airline revenue sharing game with grey demand data

Collaborative airline revenue sharing game with grey demand data

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

An Optimized Forecasting Approach Based on Grey Theory and Cuckoo Search Algorithm: A Case Study for Electricity Consumption in New South Wales

Contact Info

Product

Resources

About