Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8651

Browse

Filters

2006 - 200912010 - 20205

Settings

Subject: search.filters.subject.Forecasting

Search Results

Now showing 1 - 6 of 6
  • 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 Mühendisliği
    Average 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: 21
    Forecasting 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: 33
    Citation - Scopus: 40
    A Novel R/S Fractal Analysis and Wavelet Entropy Characterization Approach for Robust Forecasting Based on Self-Similar Time Series Modeling
    (World Scientific Publ Co Pte Ltd, 2020) Baleanu, Dumitru; Karaca, Yeliz
    It has become vital to effectively characterize the self-similar and regular patterns in time series marked by short-term and long-term memory in various fields in the ever-changing and complex global landscape. Within this framework, attempting to find solutions with adaptive mathematical models emerges as a major endeavor in economics whose complex systems and structures are generally volatile, vulnerable and vague. Thus, analysis of the dynamics of occurrence of time section accurately, efficiently and timely is at the forefront to perform forecasting of volatile states of an economic environment which is a complex system in itself since it includes interrelated elements interacting with one another. To manage data selection effectively and attain robust prediction, characterizing complexity and self-similarity is critical in financial decision-making. Our study aims to obtain analyzes based on two main approaches proposed related to seven recognized indexes belonging to prominent countries (DJI, FCHI, GDAXI, GSPC, GSTPE, N225 and Bitcoin index). The first approach includes the employment of Hurst exponent (HE) as calculated by Rescaled Range (R/S) fractal analysis and Wavelet Entropy (WE) in order to enhance the prediction accuracy in the long-term trend in the financial markets. The second approach includes Artificial Neural Network (ANN) algorithms application Feed forward back propagation (FFBP), Cascade Forward Back Propagation (CFBP) and Learning Vector Quantization (LVQ) algorithm for forecasting purposes. The following steps have been administered for the two aforementioned approaches: (i) HE and WE were applied. Consequently, new indicators were calculated for each index. By obtaining the indicators, the new dataset was formed and normalized by min-max normalization method' (ii) to form the forecasting model, ANN algorithms were applied on the datasets. Based on the experimental results, it has been demonstrated that the new dataset comprised of the HE and WE indicators had a critical and determining direction with a more accurate level of forecasting modeling by the ANN algorithms. Consequently, the proposed novel method with multifarious methodology illustrates a new frontier, which could be employed in the broad field of various applied sciences to analyze pressing real-world problems and propose optimal solutions for critical decision-making processes in nonlinear, complex and dynamic environments.
  • Article
    Citation - WoS: 8
    Citation - Scopus: 8
    A 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, Jinyong
    This 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.
  • Conference Object
    Citation - WoS: 13
    Citation - Scopus: 17
    An Overview of Revenue Management and Dynamic Pricing Models in Hotel Business
    (Edp Sciences S A, 2018) Bandalouski, Andrei M.; Kovalyov, Mikhail Y.; Pesch, Erwin; Tarim, S. Armagan
    Basic concepts and brief description of revenue management models and decision tools in the hotel business are presented. An overview of the relevant literature on dynamic pricing, forecasting methods and optimization models is provided. The main ideas of the authors' customized revenue management method for the hotel business are presented.
  • Conference Object
    Citation - WoS: 53
    Citation - Scopus: 56
    Fuzzy Prediction Strategies for Gene-Environment Networks - Fuzzy Regression Analysis for Two-Modal Regulatory Systems
    (Edp Sciences S A, 2016) Ozmen, Ayse; Weber, Gerhard-Wilhelm; Meyer-Nieberg, Silja; Defterli, Ozlem; Kropat, Erik
    Target-environment networks provide a conceptual framework for the analysis and prediction of complex regulatory systems such as genetic networks, eco-finance networks or sensor-target assignments. These evolving networks consist of two major groups of entities that are interacting by unknown relationships. The structure and dynamics of the hidden regulatory system have to be revealed from uncertain measurement data. In this paper, the concept of fuzzy target-environment networks is introduced and various fuzzy possibilistic regression models are presented. The relation between the targets and/or environmental entities of the regulatory network is given in terms of a fuzzy model. The vagueness of the regulatory system results from the (unknown) fuzzy coefficients. For an identification of the fuzzy coefficients' shape, methods from fuzzy regression are adapted and made applicable to the bi-level situation of target-environment networks and uncertain data. Various shapes of fuzzy coefficients are considered and the control of outliers is discussed. A first numerical example is presented for purposes of illustration. The paper ends with a conclusion and an outlook to future studies.