Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651
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Conference Object Forecasting Day of Week Volume Fluctuations in the Intermodal Freight Transportation(Institute of Industrial Engineers, 2011) Ertem, M.A.; Ertem, Mustafa Alp; Endüstri MühendisliğiAverage daily volume fluctuates intensely based on the day of week in the intermodal freight transportation. Shippers tend to peak around Thursdays and receivers tend to peak around Mondays. These fluctuations bring challenges to the industry in terms of capacity management and getting reliable service from the railroad companies. The purpose of this study is to forecast J. B. Hunt Transport Services, Inc.'s load volume on railroads. Load is meant to be the number of containers that will arrive at a rail ramp during a 24hrs time window. The end in mind is to have better service from the railroad companies and to manage the company owned equipment better. The forecasting model applied to tackle this problem is a multiple linear regression model and is based on the historical in-gate numbers. It uses the previous two year's data and day of week information as independent variables, and current year's data as the response variable. The results indicate better accuracy levels for the model when compared to the two week moving average.Article Citation - Scopus: 21Forecasting Stock Market Volatility: Further International Evidence(2006) Balaban, E.; Bayar, A.; Faff, R.W.This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility in fifteen stock markets. Volatility is defined as within-month standard deviation of continuously compounded daily returns on the stock market index of each country for the ten-year period 1988 to 1997. The first half of the sample is retained for the estimation of parameters while the second half is for the forecast period. The following models are employed: a random walk model, a historical mean model, moving average models, weighted moving average models, exponentially weighted moving average models, an exponential smoothing model, a regression model, an ARCH model, a GARCH model, a GJR-GARCH model, and an EGARCH model. First, standard (symmetric) loss functions are used to evaluate the performance of the competing models: mean absolute error, root mean squared error, and mean absolute percentage error. According to all of these standard loss functions, the exponential smoothing model provides superior forecasts of volatility. On the other hand, ARCH-based models generally prove to be the worst forecasting models. Asymmetric loss functions are employed to penalize under-/over-prediction. When under-predictions are penalized more heavily, ARCH-type models provide the best forecasts while the random walk is worst. However, when over-predictions of volatility are penalized more heavily, the exponential smoothing model performs best while the ARCH-type models are now universally found to be inferior forecasters.Article Citation - WoS: 8Citation - Scopus: 8A Novel Fractional Grey Model Applied To the Environmental Assessment in Turkey(World Scientific Publ Co Pte Ltd, 2020) Arshad, Sadia; Defterli, Ozlem; Xie, Xiaoqing; Baleanu, Dumitru; Shaheen, Aliya; Sheng, JinyongThis study presents a novel fractional order grey model FGM (alpha,1) obtained by extending the grey model (GM (1,1)). For this, we generalize the whitenization first-order differential equation to fractional order by using the Caputo fractional derivative of order alpha. A real-world case study, scrutinize the economic growth influence on environmental degradation in Turkey, is performed to evaluate the significance of the projected model FGM (alpha,1) in contrast to the current classical GM. We apply autoregressive distributed lags bounds testing co-integration approach to empirically examine the long-run and short-run relation among economic growth, agriculture, forestry and fishing (AFF), electricity utilization and CO2 emissions. Using the new fractional order model, all the variables are forecasted in the forthcoming years until 2030. Findings disclose that electricity utilization and economic growth (GDP) accelerate emission of CO2 though in the long run agriculture, forestry, and fishing reduce the environmental pollution in Turkey.