Turkish journal of forecasting最新文献

Development of Comparative Forecasting Models of Daily Prices of Aggressive Pension Mutual Funds by Univariate Time Series Methods 利用单变量时间序列方法建立进取型养老金共同基金每日价格的比较预测模型

Turkish journal of forecasting Pub Date : 2024-07-05 DOI: 10.34110/forecasting.1465436

Simge Eşsiz, M. Ordu

{"title":"Development of Comparative Forecasting Models of Daily Prices of Aggressive Pension Mutual Funds by Univariate Time Series Methods","authors":"Simge Eşsiz, M. Ordu","doi":"10.34110/forecasting.1465436","DOIUrl":"https://doi.org/10.34110/forecasting.1465436","url":null,"abstract":"The primary goal of the individual pension system is to enhance retirees' living standards by generating supplementary income through the investment of their savings during retirement. This involves guiding individuals to invest their savings in pension mutual funds. This research aims to develop comparative forecasting models using Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing techniques for the daily prices of pension mutual funds categorized as aggressive risk. The study utilizes data from 2020 to 2023 pertaining to a pension mutual fund provided by a Turkish pension company. The dataset is split into a training set (75%) and a validation set (25%). Mean Absolute Percentage Error (MAPE) is employed to gauge the error measurement values of the training and validation sets of the developed forecasting models. The findings reveal that for the validation sets, ARIMA model performs best for the İş Bank participation index funds, whereas Exponential Smoothing forecasting models yield the lowest MAPE values for equity, group equity, and secondary equity funds. This research can serve as a decision-making tool for the effective management of high-yield pension mutual funds and aid pension companies in enhancing the appeal of their product offerings to customers.","PeriodicalId":494740,"journal":{"name":"Turkish journal of forecasting","volume":" 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141675952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bitcoin Trend Reversal Prediction with Tree-Based Ensemble Machine Learning 利用树状集合机器学习预测比特币趋势反转

Turkish journal of forecasting Pub Date : 2024-03-27 DOI: 10.34110/forecasting.1390292

Sergül Ürgenç, Barış Aşıkgil

引用次数: 0

Comparison of Machine Learning Algorithms for Predicting Financial Risk in Cash Flow Statements 预测现金流量表中财务风险的机器学习算法比较

Turkish journal of forecasting Pub Date : 2024-02-26 DOI: 10.34110/forecasting.1403565

Ecem Engi̇n, Damla İLTER FAKHOURI

引用次数: 0

Metaheuristic algorithms to forecast future carbon dioxide emissions of Turkey 预测土耳其未来二氧化碳排放量的元启发式算法

Turkish journal of forecasting Pub Date : 2024-02-16 DOI: 10.34110/forecasting.1388906

O. A. Arık, Erkan Köse, Gülçin Canbulut

{"title":"Metaheuristic algorithms to forecast future carbon dioxide emissions of Turkey","authors":"O. A. Arık, Erkan Köse, Gülçin Canbulut","doi":"10.34110/forecasting.1388906","DOIUrl":"https://doi.org/10.34110/forecasting.1388906","url":null,"abstract":"This paper proposes the use of five different metaheuristic algorithms for forecasting carbon dioxide emissions (MtCO2) in Turkey for the years between 2019 and 2030. Historical economic indicators and construction permits in square meters of Turkey between 2002 and 2018 are used as independent variables in the forecasting equations, which take the form of two multiple linear regression models: a linear and a quadratic model. The proposed metaheuristic algorithms, including Artificial Bee Colony (ABC), Genetic Algorithm (GA), Simulated Annealing (SA), as well as hybrid versions of ABC with SA and GA with SA, are used to determine the coefficients of these regression models with reduced statistical error. The forecasting performance of the proposed methods is compared using multiple statistical methods, and the results indicate that the hybrid version of ABC with SA outperforms other methods in terms of statistical error for the linear equation model, while the hybrid version of GA with SA performs better for the quadratic equation model. Finally, four different scenarios are generated to forecast the future carbon dioxide emissions of Turkey. These scenarios reveal that if construction permits and the population is strictly managed while the economical wealth of Turkey keeps on improving, the CO2 emissions of Turkey may be less than in other possible cases.","PeriodicalId":494740,"journal":{"name":"Turkish journal of forecasting","volume":"56 45","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139960835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Mass Detection in Mammography Using Focal Loss Based RetinaNet 基于视网膜网改善乳腺x线造影肿块检测

Turkish journal of forecasting Pub Date : 2023-10-17 DOI: 10.34110/forecasting.1326245

Semih DEMİREL, Ataberk URFALI, Ömer Faruk BOZKIR, Azer ÇELİKTEN, Abdulkadir BUDAK, Hakan KARATAŞ

{"title":"Improving Mass Detection in Mammography Using Focal Loss Based RetinaNet","authors":"Semih DEMİREL, Ataberk URFALI, Ömer Faruk BOZKIR, Azer ÇELİKTEN, Abdulkadir BUDAK, Hakan KARATAŞ","doi":"10.34110/forecasting.1326245","DOIUrl":"https://doi.org/10.34110/forecasting.1326245","url":null,"abstract":"Breast cancer is a significant global health issue and plays a crucial role in improving patient outcomes through early detection. This study aims to enhance the accuracy and efficiency of breast cancer diagnosis by investigating the application of the RetinaNet algorithm for mass detection in mammography images. A specialized dataset was created for mass detection from mammography images and validated by an expert radiologist. The dataset was trained using RetinaNet, a state-of-the-art object detection model. The training and testing were conducted using the Detectron2 platform. To avoid overfitting during training, data augmentation techniques available in the Detectron2 platform were used. The model was tested using the AP50, precision, recall, and F1-Score metrics. The results of the study demonstrate the success of RetinaNet in mass detection. According to the obtained results, an AP50 value of 0.568 was achieved. The precision and recall performance metrics are 0.735 and 0.60 respectively. The F1-Score metric, which indicates the balance between precision and recall, obtained a value of 0.66. These results demonstrate that RetinaNet can be a potential tool for breast cancer screening and has the potential to provide accuracy and efficiency in breast cancer diagnosis. The trained RetinaNet model was integrated into existing PACS (Picture Archiving and Communication System) systems and made ready for use in healthcare centers.","PeriodicalId":494740,"journal":{"name":"Turkish journal of forecasting","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135944186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0