Performance Comparison of Holt-Winters and SARIMA Models for Tourism Forecasting in Turkey

Forecasting the number of tourists coming to Turkey can play a vital role in strategic planning for both private and public sectors. In this study, monthly data of foreigners visiting Turkey were collected between the years 2007 and 2018. The data showed a seasonal behavior with an increasing trend; consequently, two methods were chosen for the study: Holt-Winters (HW) and Seasonal Autoregressive Integrated Moving Average (SARIMA). The objective of the study is to determine the most appropriate forecasting model to achieve a good level of forecasting accuracy. The findings showed that all models provided accurate forecast values according to error measures. However, multiplicative model of HW achieved the highest forecasting accuracy followed by SARIMA and additive HW respectively

Performance Comparison of Holt-Winters and SARIMA Models for Tourism Forecasting in Turkey

Forecasting the number of tourists coming to Turkey can play a vital role in strategic planning for both private and public sectors. In this study, monthly data of foreigners visiting Turkey were collected between the years 2007 and 2018. The data showed a seasonal behavior with an increasing trend; consequently, two methods were chosen for the study: Holt-Winters (HW) and Seasonal Autoregressive Integrated Moving Average (SARIMA). The objective of the study is to determine the most appropriate forecasting model to achieve a good level of forecasting accuracy. The findings showed that all models provided accurate forecast values according to error measures. However, multiplicative model of HW achieved the highest forecasting accuracy followed by SARIMA and additive HW respectively

Procedural tool for analysing building energy performance: structural equation modelling protocol

Building energy performance assessment technique has become a new paradigm that plays a significant part in reducing world energy demand and greenhouse gas emissions. However, there exists a global proliferation of diverse models for assessing and benchmarking buildings. This paper proposes a single building energy performance assessment model that considered several factors that affect office building energy efficiency performances in two different countries. It aimed to develop a model that could identify building energy performance critical factors as a new technique for aggregating energy efficiency metrics for commercial buildings. It examined the relationship and interdependency between the variables as it affects buildings’ performance as a basis for developing its theoretical model. Survey questions were derived from variables obtained from the existing literature using this theoretical paper proposition. A self-administered questionnaire was used to gather data from occupants of office buildings in Nigeria and the UK. Exploratory factor analysis and structural equation modelling via confirmatory factor analysis were used to analyse the explanatory power of the measured variables and their constructs. The results identified management, strategic and operational issues as critical factors that affect building energy performance in both countries. It confirmed the relationships and interdependency of the study factors and developed a new strategy that gives them proper considerations in the operations and management of building energy. Data collected support the theoretical model, and the measurement model fits into the conceptual model. The model gives a quantitative approach that identified critical factors for improving energy management and auditing efficiency of buildings

Comparing performances of clements, box-cox, Johnson methods with weibull distributions for assessing process capability

Purpose: This study examines Clements’ Approach (CA), Box-Cox transformation (BCT), and Johnson transformation (JT) methods for process capability assessments through Weibull-distributed data with different parameters to figure out the effects of the tail behaviours on process capability and compares their estimation performances in terms of accuracy and precision. Design/methodology/approach: Usage of process performance index (PPI) Ppu is handled for process capability analysis (PCA) because the comparison issues are performed through generating Weibull data without subgroups. Box plots, descriptive statistics, the root-mean-square deviation (RMSD), which is used as a measure of error, and a radar chart are utilized all together for evaluating the performances of the methods. In addition, the bias of the estimated values is important as the efficiency measured by the mean square error. In this regard, Relative Bias (RB) and the Relative Root Mean Square Error (RRMSE) are also considered. Findings: The results reveal that the performance of a method is dependent on its capability to fit the tail behavior of the Weibull distribution and on targeted values of the PPIs. It is observed that the effect of tail behavior is more significant when the process is more capable. Research limitations/implications: Some other methods such as Weighted Variance method, which also give good results, were also conducted. However, we later realized that it would be confusing in terms of comparison issues between the methods for consistent interpretations. Practical implications: Weibull distribution covers a wide class of non-normal processes due to its capability to yield a variety of distinct curves based on its parameters. Weibull distributions are known to have significantly different tail behaviors, which greatly affects the process capability. In quality and reliability applications, they are widely used for the analyses of failure data in order to understand how items are failing or failures being occurred. Many academicians prefer the estimation of long term variation for process capability calculations although Process Capability Indices (PCIs) Cp and Cpk are widely used in literature. On the other hand, in industry, especially in automotive industry, the PPIs Pp and Ppk are used for the second type of estimations. Originality/value: Performance comparisons are performed through generating Weibull data without subgroups and for this reason, process performance indices (PPIs) are executed for computing process capability rather than process capability indices (PCIs). Box plots, descriptive statistics, the root-mean-square deviation (RMSD), which is used as a measure of error, and a radar chart are utilized all together for evaluating the performances of the methods. In addition, the bias of the estimated values is important as the efficiency measured by the mean square error. In this regard, Relative Bias (RB) and the Relative Root Mean Square Error (RRMSE) are also considered. To the best of our knowledge, all these issues including of execution of PPIs are performed all together for the first time in the literature

An evolutionary multi-objective optimization approach to disaster waste management: a case study of Istanbul, Turkey

Disasters can lead to a high risk of casualties and structural damages. Destructive disasters, such as earthquakes, may cause a great amount of disaster waste to be controlled. Reusing and recycling materials in the debris can decrease the need for re-construction resources. These reusable or recyclable materials can be processed in temporary storage sites like the ones suggested by the United Nations and the United States Federal Emergency Management Agency guidelines on disaster waste management. The objective of this paper is to build a framework for determining the locations of temporary storage facilities, and includes planning for the collection and transportation of disaster waste in order to manage it in an environmentally sustainable way. In this study, a multi-objective optimization model is developed and solved with an evolutionary elitist multi-objective optimization algorithm (NSGA-II). As a city prone to high earthquake damage, Istanbul has been selected for the illustration of the proposed framework. The objectives of the model are cost minimization and minimization of risk from hazardous waste exposure. The study integrates disaster loss estimation methods with post-disaster waste management. © 2015 Elsevier Ltd. All rights reserved

Intelligent Association Rules for Innovative SME Collaboration

Part 3: Techniques of Artificial Intelligence Supporting Knowledge ManagementInternational audienceSMEs are encouraged to collaborate for research and innovation in order to survive in tough global competition. Even the technology SMEs with high knowledge capital have the fear to collaborate with other SMEs or bigger companies. This study aims to illuminate the preferences in customer, supplier and competitor collaboration within industry or inter industry. A survey is run on more than 110 companies and Machine Learning methods are used to define the association rules that will lead for success