Turkish Journal of Forecasting最新文献

Heart Attack Analysis and Prediction with Machine Learning Techniques 利用机器学习技术分析和预测心脏病发作

Turkish Journal of Forecasting Pub Date : 2024-07-06 DOI: 10.34110/forecasting.1489839

Shuaib Jasim, İbrahim Onaran, Mustafa Al-asadi

引用次数: 0

Forecasting of Turkey's Hazelnut Export Amounts According to Seasons with Dendritic Neuron Model Artificial Neural Network 用树枝状神经元模型人工神经网络根据季节预测土耳其榛子出口量

Turkish Journal of Forecasting Pub Date : 2024-06-12 DOI: 10.34110/forecasting.1468420

Emine Kölemen

{"title":"Forecasting of Turkey's Hazelnut Export Amounts According to Seasons with Dendritic Neuron Model Artificial Neural Network","authors":"Emine Kölemen","doi":"10.34110/forecasting.1468420","DOIUrl":"https://doi.org/10.34110/forecasting.1468420","url":null,"abstract":"It is seen that artificial neural networks have begun to be used extensively in the literature in solving the time series forecasting problem. In addition to artificial neural networks, classical forecasting methods can often be used to solve this problem. It is seen that classical forecasting methods give successful results for linear time series analysis. However, there is no linear relationship in many time series. Therefore, it can be thought that deep artificial neural networks, which contain more parameters but create more flexible non-linear model structures compared to classical time series forecasting methods, may enable the production of more successful forecasting methods. In this study, the problem of forecasting hazelnut export amounts according to seasons in Turkey with a dendritic neuron model artificial neural network is discussed. In this study, a training algorithm based on the particle swarm optimization algorithm is given for training the dendritic neuron model artificial neural network. The motivation of the study is to investigate Turkey's hazelnut export amounts according to seasons, using a dendritic neuron model artificial neural network. The performance of the proposed method has been compared with artificial neural networks used in the literature.","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141354955","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

Forecasting of Giresun Hazelnut Quantity in Giresun Province Using Pi-Sigma Artificial Neural Networks 利用 Pi-Sigma 人工神经网络预测吉雷松省榛子数量

Turkish Journal of Forecasting Pub Date : 2024-06-12 DOI: 10.34110/forecasting.1468419

Özlem Karahasan

{"title":"Forecasting of Giresun Hazelnut Quantity in Giresun Province Using Pi-Sigma Artificial Neural Networks","authors":"Özlem Karahasan","doi":"10.34110/forecasting.1468419","DOIUrl":"https://doi.org/10.34110/forecasting.1468419","url":null,"abstract":"Artificial neural networks are frequently used to solve many problems and give successful results. Artificial neural networks, which we frequently encounter in solving forecasting problems, attract the attention of researchers with the successful results they provide. Pi-sigma artificial neural network, which is a high-order artificial neural network, draws attention with its use of both additive and multiplicative combining functions in its architectural structure. This artificial neural network model offers successful forecasting results thanks to its high-order structures. In this study, the pi-sigma artificial neural network was preferred due to its superior performance properties, and the particle swarm optimization algorithm was used for training the pi-sigma artificial neural network. To evaluate the performance of this preferred artificial neural network, monthly ready-made manufacturer sale shelled hazelnut quantities in Giresun province was used and a comparison was made with many artificial neural network models available in the literature. It has been observed that this tested method has the best performance among other compared methods.","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"143 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141350863","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

Hybrid AI-based Voice Authentication 基于人工智能的混合语音认证

Turkish Journal of Forecasting Pub Date : 2023-12-17 DOI: 10.34110/forecasting.1260073

Bilal Bora, Ahmet Emin Emanet, Enes Elmaci, Derya Kandaz, Muhammed Kürşad Uçar

引用次数: 0

Revised Passing-Bablok Regression Method for Model Comparison 用于模型比较的经过修订的 Passing-Bablok 回归方法

Turkish Journal of Forecasting Pub Date : 2023-11-26 DOI: 10.34110/forecasting.1367369

N. Erilli

引用次数: 0

Impact of Structural Break Location on Forecasting Accuracy: Traditional Methods Versus Artificial Neural Network 结构断裂位置对预测精度的影响:传统方法与人工神经网络

Turkish Journal of Forecasting Pub Date : 2022-12-30 DOI: 10.34110/forecasting.1162548

Daud Aser, E. Firuzan

{"title":"Impact of Structural Break Location on Forecasting Accuracy: Traditional Methods Versus Artificial Neural Network","authors":"Daud Aser, E. Firuzan","doi":"10.34110/forecasting.1162548","DOIUrl":"https://doi.org/10.34110/forecasting.1162548","url":null,"abstract":"Since forecasting the future values is fundamental for researchers, investors, practitioners, etc., obtaining accurate predictions is critical in time series analysis. The accuracy is reliant on good modeling and good quality data. The latter is affected by unusual observations, changes over time, missing data, and structural breaks among others. Economic crises are the major cause of data instability and therefore, this paper focuses on how structural breaks in conditional heteroscedastic financial and macroeconomic data affect forecasting accuracy on short and long-term horizons. More specifically, we are interested in the impact of the location of the structural break and break size on the predictive performance of two linear (ARIMA and Exponential Smoothing) forecasting models and two nonlinear (ARIMA – ARCH and Artificial Neural Network) models. We conducted Monte Carlo simulations and showed that the forecasting accuracy decreases as the structural break location approaches the end of the sample. In addition, break size and length of the horizon significantly impact the forecasting accuracy. We also showed that ARIMA – ARCH model is the best performing in the absence of structural break while the artificial neural network model outperforms all the competing models in the presence of structural break, especially in large break sizes and long horizons. Last, we applied the above techniques to forecasting daily close prices of Brent oil and Turkish Lira – USD exchange rates out–of–sample and similar results were found.","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126700285","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

Prediction of the Premium Production of Some Insurance Companies Operating in Turkey with Artificial Neural Networks 人工神经网络对土耳其部分保险公司保费收入的预测

Turkish Journal of Forecasting Pub Date : 2022-12-29 DOI: 10.34110/forecasting.1223653

Buse Özgür, U. Yolcu

{"title":"Prediction of the Premium Production of Some Insurance Companies Operating in Turkey with Artificial Neural Networks","authors":"Buse Özgür, U. Yolcu","doi":"10.34110/forecasting.1223653","DOIUrl":"https://doi.org/10.34110/forecasting.1223653","url":null,"abstract":"The insurance sector can be seen as a sector that directly affects the country's economy and development with its ability to fund financial markets and meet risks. In this respect, predicting the premium sizes, which is the main factor that constitutes the volume of the insurance sector, as accurately and reliably as possible, indirectly means foreseeing the risks that may arise in terms of the economy and development of the country and taking the necessary measures. In this study, the premium production of some insurance companies operating in Turkey is predicted with different artificial neural networks and evaluated the results comparatively. In this context, basically, two different artificial neural networks (ANNs), feed-forward, and feed-back have been used as predictive tools for insurance premium production. Two training algorithms and two different activation functions have been operated in the structure of the ANNs used. Thus, eight different predictive tools for insurance companies' premium production have been created. The prediction performances of ANNs have been evaluated on the test sets using error criteria such as Root Mean Error Squares, Average Absolute Percentile Error, and Median Absolute Percentile Error.","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131099897","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

Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality 基于CT模式的全自动端到端卷积神经网络胰腺肿瘤分割

Turkish Journal of Forecasting Pub Date : 2022-12-06 DOI: 10.34110/forecasting.1190299

Ahmet Furkan Bayram, Caglar Gurkan, Abdulkadir Budak, Hakan Karatas

引用次数: 0

Convolutional Neural Networks for MRI-Based Brain Tumor Segmentation: A Comparative Analysis of State-of-the-Art Segmentation Networks 基于核磁共振成像的卷积神经网络脑肿瘤分割:最新分割网络的比较分析

Turkish Journal of Forecasting Pub Date : 2022-11-04 DOI: 10.34110/forecasting.1190289

Ahmet Furkan Bayram, Caglar Gurkan, Abdulkadir Budak, Hakan Karatas

引用次数: 1

The Effect of Handling Imbalanced Datasets Methods on Prediction of Entrepreneurial Competency in University Students 不平衡数据集处理方法对大学生创业胜任力预测的影响

Turkish Journal of Forecasting Pub Date : 2022-11-01 DOI: 10.34110/forecasting.1185545

Murat Simsek, Ahmet Said Daş

{"title":"The Effect of Handling Imbalanced Datasets Methods on Prediction of Entrepreneurial Competency in University Students","authors":"Murat Simsek, Ahmet Said Daş","doi":"10.34110/forecasting.1185545","DOIUrl":"https://doi.org/10.34110/forecasting.1185545","url":null,"abstract":"As of today entrepreneurs and entrepreneurship are considered to be the integral parts of the economic and technological advancements. Entrepreneurs are promoted in many countries because of their high return on investment opportunities both in terms of income and new inventions. \u0000Numerous studies prove that entrepreneurs have many traits in common and these common traits can correlate with each other. Based on these common traits, potential entrepreneurs can be predicted, current entrepreneurs can be improved by realising their weak sides and the ones who wish to be entrepreneurs can be provided with insights. A machine learning approach can light the way for a better rewarding future for entrepreneurship, helping these goals significantly. \u0000There exist several studies for the prediction of entrepreneurial competency with the use of machine learning algorithms. Most machine learning methods perform better accuracy and F1-score imbalanced data instead in imbalanced data. This study focuses on utilizing imbalanced class handling methods to increase prediction performance. Random Oversampling, Random Undersampling, SMOTE, and NearMiss methods are used to handling imbalanced data for this purpose in this study. The performance of the machine learning algorithms with Imbalanced Data Handling methods is compared with the machine learning algorithms. Comparisons were made using accuracy, precision, recall, F1-Score as performance parameters. The comparison shows a noticeable performance increase using machine learning algorithms with handling imbalanced dataset methods.","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115003454","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