Pub Date : 2020-12-16DOI: 10.34110/forecasting.778616
N. Erilli
Regression analysis is one of the well-known methods of multivariate analysis and it is efficiently used in many research fields, especially forecasting problems. In order for the results of regression analysis to be effective, some assumptions must be valid. One of these assumptions is the heterogeneity problem. One of the methods used to solve this problem is the weighted regression method. Weighted regression is a useful method when one of the least-squares assumptions of constant variance in the residuals is violated (heteroscedasticity). This procedure can minimize the sum of weighted squared residuals to produce residuals with a uniform variance if the appropriate weight will be used. (homoscedasticity). In this study, the Gustafson-Kessel method, one of the fuzzy clustering analysis method, is used to determine weights for weighted regression analysis. GustafsonKessel's method is based on the minimization of the sum of weighted squared distances which is used Mahalanobis distance, between the data points and the cluster centres. With the fuzzy clustering method, each observation value is bound to the specified clusters in a specific order of membership. These membership degrees will be calculated as weights in the weighted regression analysis and estimation work will be done. In application, 5 simulation and 1 real-time data were estimated by the proposed method. The results were interpreted by comparing with Robust Methods (M and S estimator) and weighted with FCM Regression analysis. 2020 Turkish Journal of Forecasting by Giresun University, Forecast Research Laboratory is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
{"title":"Fuzzy Clustering Based Regression Model Forecasting: Weighted By Gustafson-Kessel Algorithm","authors":"N. Erilli","doi":"10.34110/forecasting.778616","DOIUrl":"https://doi.org/10.34110/forecasting.778616","url":null,"abstract":"Regression analysis is one of the well-known methods of multivariate analysis and it is efficiently used in many research fields, especially forecasting problems. In order for the results of regression analysis to be effective, some assumptions must be valid. One of these assumptions is the heterogeneity problem. One of the methods used to solve this problem is the weighted regression method. Weighted regression is a useful method when one of the least-squares assumptions of constant variance in the residuals is violated (heteroscedasticity). This procedure can minimize the sum of weighted squared residuals to produce residuals with a uniform variance if the appropriate weight will be used. (homoscedasticity). In this study, the Gustafson-Kessel method, one of the fuzzy clustering analysis method, is used to determine weights for weighted regression analysis. GustafsonKessel's method is based on the minimization of the sum of weighted squared distances which is used Mahalanobis distance, between the data points and the cluster centres. With the fuzzy clustering method, each observation value is bound to the specified clusters in a specific order of membership. These membership degrees will be calculated as weights in the weighted regression analysis and estimation work will be done. In application, 5 simulation and 1 real-time data were estimated by the proposed method. The results were interpreted by comparing with Robust Methods (M and S estimator) and weighted with FCM Regression analysis. 2020 Turkish Journal of Forecasting by Giresun University, Forecast Research Laboratory is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123124075","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}
Pub Date : 2019-12-31DOI: 10.34110/forecasting.660377
Bakiye Kılıç Topuz, M. Bozoğlu, Nevra Alhas Eroğlu, Uğur Başer
In 2017, 144 countries in the world produced 97.862.928 tons onion at 5.201.591 hectares. Turkey produced 2.1 million tons onion in 68 thousand hectares. Turkey was the seventh-largest producer country of dry onion with a share of 2,18% in the world. The main aim of this research was to forecast the onion area and production of Turkey for the period of 2019-2026. The data of this study was obtained from the database of the Food and Agriculture Organization and the time series covered the period of 1961-2018. Three Exponential Smoothing Methods were compared to model onion area and production and Holt Exponential Smoothing model was determined as the most appropriate forecasting model. In the study, time series data were determined as non-stationary and so, stationarity was obtained after taking the first difference of the time series. Model results show that, in the 2019-2026 period, the forecasted sown area of onion would be increased from 58.873 hectares to 60.981 hectares, forecasted production of onion would be increased from 2.066.453 tons to 2.309.751 tons. In order to reduce the effect of Cobweb theorem, onion production should be planned by producer organizations. The supply gap can be avoided by taking appropriate policy measures and this is necessary to maintain Turkey’s position in the world onion market.
{"title":"Forecasting of Onion Sown Area and Production in Turkey Using Exponential Smoothing Method","authors":"Bakiye Kılıç Topuz, M. Bozoğlu, Nevra Alhas Eroğlu, Uğur Başer","doi":"10.34110/forecasting.660377","DOIUrl":"https://doi.org/10.34110/forecasting.660377","url":null,"abstract":"In 2017, 144 countries in the world produced 97.862.928 tons onion at 5.201.591 hectares. Turkey produced 2.1 million tons onion in 68 thousand hectares. Turkey was the seventh-largest producer country of dry onion with a share of 2,18% in the world. The main aim of this research was to forecast the onion area and production of Turkey for the period of 2019-2026. The data of this study was obtained from the database of the Food and Agriculture Organization and the time series covered the period of 1961-2018. Three Exponential Smoothing Methods were compared to model onion area and production and Holt Exponential Smoothing model was determined as the most appropriate forecasting model. In the study, time series data were determined as non-stationary and so, stationarity was obtained after taking the first difference of the time series. Model results show that, in the 2019-2026 period, the forecasted sown area of onion would be increased from 58.873 hectares to 60.981 hectares, forecasted production of onion would be increased from 2.066.453 tons to 2.309.751 tons. In order to reduce the effect of Cobweb theorem, onion production should be planned by producer organizations. The supply gap can be avoided by taking appropriate policy measures and this is necessary to maintain Turkey’s position in the world onion market.","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129716235","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}
Pub Date : 2019-08-31DOI: 10.34110/forecasting.484765
Irfan Macit
Earthquake is one of the natural disaster types that suddenly breaks regular human life. Rescue activities in disasters are one of the most critical stages of modern disaster management. This management stage, as mentioned earlier, includes all the activities that need to be done after the disaster. Search And Rescue (SAR) teams perform one of these most critical activities after the earthquake post-disaster period. Search and rescue teams that will rescue and relief after a disaster are selected according to the criteria selected. Location layout selection problems are NP-Hard, and obtaining hard results is in the class of these problems. One of these criteria is the Risk Pressure Factor (RPF) used in determining the priorities of the risk areas. Determining the level of risk level is very difficult and also these are difficult to predict. In this study, it is aimed to estimate this parametric value by using an artificial neural network (ANN) method which is applied in many fields. And then in this study, a prediction model was constructed by using back propagation method which is a suitable propagation method in ANN method and results are obtained from the MATLAB program. The resulting risk-pressure factor (RPF) value can be used as a parameter in the proposed mathematical model. As a result of the study, the missing parameter of the mathematical model will be found in the estimation of a parameter belonging to the proposed mathematical model.
{"title":"Estimating Risk Pressure Factor (RPF) with Artificial Neural Network (ANN) to Locate Search and Rescue (SAR) Team Station.","authors":"Irfan Macit","doi":"10.34110/forecasting.484765","DOIUrl":"https://doi.org/10.34110/forecasting.484765","url":null,"abstract":"Earthquake is one of the natural disaster types that suddenly breaks regular human life. Rescue activities in disasters are one of the most critical stages of modern disaster management. This management stage, as mentioned earlier, includes all the activities that need to be done after the disaster. Search And Rescue (SAR) teams perform one of these most critical activities after the earthquake post-disaster period. Search and rescue teams that will rescue and relief after a disaster are selected according to the criteria selected. Location layout selection problems are NP-Hard, and obtaining hard results is in the class of these problems. One of these criteria is the Risk Pressure Factor (RPF) used in determining the priorities of the risk areas. Determining the level of risk level is very difficult and also these are difficult to predict. In this study, it is aimed to estimate this parametric value by using an artificial neural network (ANN) method which is applied in many fields. And then in this study, a prediction model was constructed by using back propagation method which is a suitable propagation method in ANN method and results are obtained from the MATLAB program. The resulting risk-pressure factor (RPF) value can be used as a parameter in the proposed mathematical model. As a result of the study, the missing parameter of the mathematical model will be found in the estimation of a parameter belonging to the proposed mathematical model.","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127216728","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}
Pub Date : 2019-08-31DOI: 10.34110/forecasting.514761
Barnabe Ndabashinze, Gülesen Üstündağ Şiray, L. Scrucca
Fractional polynomials are powerful statistic tools used in multivariable building model to select relevant variables and their functional form. This selection of variables, together with their corresponding power is performed through a multivariable fractional polynomials (MFP) algorithm that uses a closed test procedure, called function selection procedure (FSP), based on the statistical significance level α. In this paper, Genetic algorithms, which are stochastic search and optimization methods based on string representation of candidate solutions and various operators such as selection, crossover and mutation; reproducing genetic processes in nature, are used as alternative to MFP algorithm to select powers in an extended set of real numbers (to be specified) by minimizing the Bayesian Information Criteria (BIC). A simulation study and an application to a real dataset are performed to compare the two algorithms in many scenarios. Both algorithms perform quite well in terms of mean square error with Genetic algorithms that yied a more parsimonious model comparing to MFP Algorithm .
{"title":"Genetic Algorithms Applied to Fractional Polynomials for Power Selection: Application to Diabetes Data","authors":"Barnabe Ndabashinze, Gülesen Üstündağ Şiray, L. Scrucca","doi":"10.34110/forecasting.514761","DOIUrl":"https://doi.org/10.34110/forecasting.514761","url":null,"abstract":"Fractional polynomials are powerful statistic tools used in multivariable building model to select relevant variables and their functional form. This selection of variables, together with their corresponding power is performed through a multivariable fractional polynomials (MFP) algorithm that uses a closed test procedure, called function selection procedure (FSP), based on the statistical significance level α. In this paper, Genetic algorithms, which are stochastic search and optimization methods based on string representation of candidate solutions and various operators such as selection, crossover and mutation; reproducing genetic processes in nature, are used as alternative to MFP algorithm to select powers in an extended set of real numbers (to be specified) by minimizing the Bayesian Information Criteria (BIC). A simulation study and an application to a real dataset are performed to compare the two algorithms in many scenarios. Both algorithms perform quite well in terms of mean square error with Genetic algorithms that yied a more parsimonious model comparing to MFP Algorithm .","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126874705","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}
Pub Date : 2018-12-31DOI: 10.34110/forecasting.482914
Bakiye Kılıç Topuz, M. Bozoğlu, Uğur Başer, Nevra Alhas Eroğlu
Turkey is the first largest apricot producer in the world. In 2016, Turkey was responsible for 9,21% of world apricot production with 730 thousand tons. Turkey also generated 11,31% of world apricot exports in 2016. The main aim of this research was to forecast apricot production of Turkey for the period of 2017-2022. The data of this study was obtained from the database of the Food and Agriculture Organization and the time series covered the period of 1961-2016. Box-Jenkins Model was used to forecast apricot production. In the study, it was determined that the time series were not stationary and the series became stationary after the first difference was taken. Moving Average Model ARIMA (2, 1, 1) was determined as the most appropriate model for the stationary data type. The research results show that apricot production quantities of Turkey in 2017 was forecasted as minimum 383.206 tons, maximum 920.409 tons and, average 651.808 tons. However, Turkey’s the apricot production amount in 2022 was forecasted as minimum 271.734 tons, maximum 1.193.113 tones and average 732.423 tons. Considering the increase in demand, it is thought that apricot production will not be sufficient for the country. To protect the current leading position of the country, it is recommended that the government should give enough support to increase apricot production in Turkey.
{"title":"Forecasting of Apricot Production of Turkey by Using Box-Jenkins Method","authors":"Bakiye Kılıç Topuz, M. Bozoğlu, Uğur Başer, Nevra Alhas Eroğlu","doi":"10.34110/forecasting.482914","DOIUrl":"https://doi.org/10.34110/forecasting.482914","url":null,"abstract":"Turkey is the first largest apricot producer in the world. In 2016, Turkey was responsible for 9,21% of world apricot production with 730 thousand tons. Turkey also generated 11,31% of world apricot exports in 2016. The main aim of this research was to forecast apricot production of Turkey for the period of 2017-2022. The data of this study was obtained from the database of the Food and Agriculture Organization and the time series covered the period of 1961-2016. Box-Jenkins Model was used to forecast apricot production. In the study, it was determined that the time series were not stationary and the series became stationary after the first difference was taken. Moving Average Model ARIMA (2, 1, 1) was determined as the most appropriate model for the stationary data type. The research results show that apricot production quantities of Turkey in 2017 was forecasted as minimum 383.206 tons, maximum 920.409 tons and, average 651.808 tons. However, Turkey’s the apricot production amount in 2022 was forecasted as minimum 271.734 tons, maximum 1.193.113 tones and average 732.423 tons. Considering the increase in demand, it is thought that apricot production will not be sufficient for the country. To protect the current leading position of the country, it is recommended that the government should give enough support to increase apricot production in Turkey.","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132126325","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}
Pub Date : 2018-12-31DOI: 10.34110/forecasting.482789
Uğur Başer, M. Bozoğlu, Nevra Alhas Eroğlu, Bakiye Kılıç Topuz
Turkey is one of main producers and exporter countries of chestnut in the world. It is essential to assess scientifically the accurate future production and export potentials of chestnut on the basis of past trends. This study focuses on forecasting the chestnut production and export of Turkey up to the year 2021 using Autoregressive Integrated Moving Average (ARIMA) model. The time series data for the chestnut production and export of Turkey were obtained from the Food and Agriculture Organization of the United Nations (FAO). Annual data for the period of 1961-2016 was used for the study. The study revealed that the best models for forecasting the chestnut production and export were ARIMA (1, 1, 1) and ARIMA (1, 2, 1), respectively. The ARIMA model showed that while the chestnut production of Turkey in 2021 would be 64.183 tonnes with lower limit of 38.946 tonnes and upper limit of 89420 tonnes. However, Turkey’s chestnut export in 2021 would be 7.962 tonnes with lower limit of 563 tonnes and upper limit of 15362 tonnes. The study concluded that Turkey’s chestnut production and export will increase in the forecasted years. The stakeholders of chestnut sector should take account these projections in their production and marketing decision.
{"title":"Forecasting Chestnut Production and Export of Turkey Using ARIMA Model","authors":"Uğur Başer, M. Bozoğlu, Nevra Alhas Eroğlu, Bakiye Kılıç Topuz","doi":"10.34110/forecasting.482789","DOIUrl":"https://doi.org/10.34110/forecasting.482789","url":null,"abstract":"Turkey is one of main producers and exporter countries of chestnut in the world. It is essential to assess scientifically the accurate future production and export potentials of chestnut on the basis of past trends. This study focuses on forecasting the chestnut production and export of Turkey up to the year 2021 using Autoregressive Integrated Moving Average (ARIMA) model. The time series data for the chestnut production and export of Turkey were obtained from the Food and Agriculture Organization of the United Nations (FAO). Annual data for the period of 1961-2016 was used for the study. The study revealed that the best models for forecasting the chestnut production and export were ARIMA (1, 1, 1) and ARIMA (1, 2, 1), respectively. The ARIMA model showed that while the chestnut production of Turkey in 2021 would be 64.183 tonnes with lower limit of 38.946 tonnes and upper limit of 89420 tonnes. However, Turkey’s chestnut export in 2021 would be 7.962 tonnes with lower limit of 563 tonnes and upper limit of 15362 tonnes. The study concluded that Turkey’s chestnut production and export will increase in the forecasted years. The stakeholders of chestnut sector should take account these projections in their production and marketing decision.","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116439358","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}
Pub Date : 2018-09-01DOI: 10.34110/FORECASTING.437599
M. Zewdie, Gebretsadik G Wubit, A. W. Ayele
Among many indicators of climate change, the temperature is a key indicator to take remedial action for world global warming. This finding provides application of space-time models for temperature data, which is selected in three meteorology stations (Mekelle, Adigrat and Adwa) of Northern Ethiopia. The objectives of this research are to see the space-time variations of temperature and to find better forecasting model. The steps for building this model starting from order selection of space and autoregressive order, parameters estimation, a diagnostic check of errors and finally forecasting for the long term. The preliminary model is identified by VAR (vector autoregressive) model and tentatively selects the order by using MIC (minimum information criteria) and uses the autoregressive order for the model and fixes the spatial effect, model parameters are estimated using the least square method. Weighted matrix computed by using queen contiguity criteria. It is found that the model STAR(1,1) and GSTAR(1,1) are two options, finally the best-fitted model is GSTAR(1,1) which has high forecasting performance and smallest RMSEF. The outcome of the forecast indicated that in northern Ethiopia, the weather conditions especially temperature of future is increasing trend in dry seasons in all 3 stations in similar fashion but more consistent and has less variation across the region, and less consistent and high variation within the region and the researcher found that spatial effect has high impact on prediction of models.
在众多气候变化指标中，气温是对全球变暖采取补救措施的关键指标。这一发现为埃塞俄比亚北部三个气象站(Mekelle、Adigrat和Adwa)选择的温度数据提供了时空模型的应用。本研究的目的是了解温度的时空变化，寻找更好的预测模型。建立该模型的步骤从空间的顺序选择和自回归顺序开始，参数估计，错误诊断检查，最后进行长期预测。采用向量自回归模型VAR (vector autoregressive model)对模型进行识别，采用最小信息准则MIC (minimum information criteria)对模型进行初步阶数选择，采用自回归阶数对模型进行空间效应修正，采用最小二乘法对模型参数进行估计。采用皇后邻接准则计算加权矩阵。发现模型STAR(1,1)和GSTAR(1,1)是两种选择，最终拟合最好的模型是预测性能高、RMSEF最小的GSTAR(1,1)模型。预测结果表明，在埃塞俄比亚北部，3个站点的天气条件特别是未来温度在旱季均呈上升趋势，趋势相似，但一致性较强，区域间变化较小，区域内变化较小，一致性较差，空间效应对模式预测影响较大。
{"title":"G-STAR Model for Forecasting Space-Time Variation of Temperature in Northern Ethiopia","authors":"M. Zewdie, Gebretsadik G Wubit, A. W. Ayele","doi":"10.34110/FORECASTING.437599","DOIUrl":"https://doi.org/10.34110/FORECASTING.437599","url":null,"abstract":"Among many indicators of climate change, the temperature is a key indicator to take remedial action for world global warming. This finding provides application of space-time models for temperature data, which is selected in three meteorology stations (Mekelle, Adigrat and Adwa) of Northern Ethiopia. The objectives of this research are to see the space-time variations of temperature and to find better forecasting model. The steps for building this model starting from order selection of space and autoregressive order, parameters estimation, a diagnostic check of errors and finally forecasting for the long term. The preliminary model is identified by VAR (vector autoregressive) model and tentatively selects the order by using MIC (minimum information criteria) and uses the autoregressive order for the model and fixes the spatial effect, model parameters are estimated using the least square method. Weighted matrix computed by using queen contiguity criteria. It is found that the model STAR(1,1) and GSTAR(1,1) are two options, finally the best-fitted model is GSTAR(1,1) which has high forecasting performance and smallest RMSEF. The outcome of the forecast indicated that in northern Ethiopia, the weather conditions especially temperature of future is increasing trend in dry seasons in all 3 stations in similar fashion but more consistent and has less variation across the region, and less consistent and high variation within the region and the researcher found that spatial effect has high impact on prediction of models.","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130920744","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}
Pub Date : 2018-09-01DOI: 10.34110/FORECASTING.420126
R. A. Belaghi, Minoo Aminnejad, Ozlem Gurunlu Alma
Prediction of stock market value is one the most complicated issue during the past decades. Due to its importance, in this research, we consider the prediction of stock values based on non-parametric and parametric methods. In this first method, we use the fuzzy Markov chain procedure in order to prediction problem. In this regard, all of the rising and falling probabilities during the weekdays are calculated and then they applied to obtain the increasing and decreasing rate. Then, based on this information we model and predict the stock values. In the sequel, we implement different methods of parametric time series such as generalized autoregressive conditionally heteroskedastic (GARCH), ARIMA-GARCH, Exponential GARCH (E-GARCH) and GJR-GARCH by assuming the normal and t-student distribution for the error terms to obtain the best model in terms of minimum mean square errors. Finally, the mythologies developed here are applied for the Tehran Stock Exchange Index (TEDPIX).
{"title":"Stock Market Prediction Using Nonparametric Fuzzy and Parametric GARCH Methods","authors":"R. A. Belaghi, Minoo Aminnejad, Ozlem Gurunlu Alma","doi":"10.34110/FORECASTING.420126","DOIUrl":"https://doi.org/10.34110/FORECASTING.420126","url":null,"abstract":"Prediction of stock market value is one the most complicated issue during the past decades. Due to its importance, in this research, we consider the prediction of stock values based on non-parametric and parametric methods. In this first method, we use the fuzzy Markov chain procedure in order to prediction problem. In this regard, all of the rising and falling probabilities during the weekdays are calculated and then they applied to obtain the increasing and decreasing rate. Then, based on this information we model and predict the stock values. In the sequel, we implement different methods of parametric time series such as generalized autoregressive conditionally heteroskedastic (GARCH), ARIMA-GARCH, Exponential GARCH (E-GARCH) and GJR-GARCH by assuming the normal and t-student distribution for the error terms to obtain the best model in terms of minimum mean square errors. Finally, the mythologies developed here are applied for the Tehran Stock Exchange Index (TEDPIX).","PeriodicalId":141932,"journal":{"name":"Turkish Journal of Forecasting","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130434884","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}
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