The Forecasting of Labour Force Participation and the Unemployment Rate in Poland and Turkey Using Fuzzy Time Series Methods

Fuzzy time series methods based on the fuzzy set theory proposed by Zadeh (1965) was first introduced by Song and Chissom (1993). Since fuzzy time series methods do not have the assumptions that traditional time series do and have effective forecasting performance, the interest on fuzzy time series approaches is increasing rapidly. Fuzzy time series methods have been used in almost all areas, such as environmental science, economy and finance. The concepts of labour force participation and unemployment have great importance in terms of both the economy and sociology of countries. For this reason there are many studies on their forecasting. In this study, we aim to forecast the labour force participation and unemployment rate in Poland and Turkey using different fuzzy time series methods

HIGH ORDER FUZZY TIME SERIES MODEL AND ITS APLICATION TO IMKB

The observations of some real time series such as temperature and stock market can take different values in a day. Instead of representing the observations of these time series by real numbers, employing linguistic values or fuzzy sets can be more appropriate. In recent years, many approaches have been introduced to analyze time series consisting of observations which are fuzzy sets and such time series are called fuzzy time series. In this study, a novel approach is proposed to analyze high order fuzzy time series model. The proposed method is applied to IMKB data and the obtained results are discussed. IMKB data is also analyzed by using some other fuzzy time series methods available in the literature and obtained results are compared to results obtained from the proposed method. As a result of the comparison, it is seen that the proposed method produce accurate forecasts

Forecasting of Turkey inflation with hybrid of feed forward and recurrent artifical neural networks

Enflasyon öngörülerinin elde edilmesi önemli bir ekonomik problemdir. Öngörülerin doğru bir şekilde elde edilmesi daha doğru kararlara neden olacaktır. Enflasyon öngörüsü için literatürde çeşitli zaman serileri teknikleri kullanılmıştır. Son yıllarda zaman serisi öngörü probleminde esnek modelleme yeteneği nedeniyle, Yapay Sinir Ağları (YSA) tercih edilmektedir. Yapay sinir ağları doğrusal veya eğrisel belirli bir model kalıbı, durağanlık ve normal dağılım gibi ön koşullara ihtiyaç duymadığından herhangi bir zaman serisine kolaylıkla uygulanabilmektedir. Bu çalışmada Tüketici Fiyat Endeksi (TUFE) için ileri ve geri beslemeli yapay sinir ağları yaklaşımı kullanılarak öngörüler elde edilmiştir. Çözümlemede kullanılan YSA modellerinin öngörülerinin girdi olarak kullanıldığı, YSA’ya dayalı yeni bir melez yaklaşım önerilmiştir.Obtaining the inflation prediction is an important problem. Having this prediction accurately will lead to more accurate decisions. Various time series techniques have been used in the literature for inflation prediction. Recently, Artificial Neural Network (ANN) is being preferred in the time series prediction problem due to its flexible modeling capacity. Artificial neural network can be applied easily to any time series since it does not require prior conditions such as a linear or curved specific model pattern, stationary and normal distribution. In this study, the predictions have been obtained using the feed forward and recurrent artificial neural network for the Consumer Price Index (CPI). A new combined forecast has been proposed based on ANN in which the ANN model predictions employed in analysis were used as data

Forecasting Inflation Rates with High Order Fuzzy Time Series Approach

Enflasyon öngörülerinin elde edilmesi önemli bir ekonomik problemdir.Öngörülerin daha doğru elde edilmesi daha doğru kararlara neden olacaktır. T.C.Merkez bankası her yılın belirli dönemlerinde enflasyon raporları yayınlamaktadır.Raporlarda enflasyon beklentisi anketi sonuçları yer almaktadır. Bu çalışmada tüketicifiyat endeksi yüksek dereceli bulanık zaman serisi yaklaşımı ile öngörülmüştür. Yüksekdereceli bulanık zaman serisi modelinde ilişkilerin belirlenmesi yapay sinir ağları ileyapılmaktadır. Tüketici fiyat endeksi zaman serisi, ayrıca literatürde yer alan bazıbulanık zaman serisi yaklaşımları ile tahmin edilerek, öngörü doğruluğu açısından T.C.Merkez Bankası enflasyon beklentisi anketi sonuçları ile karşılaştırılmıştır. To obtain inflation forecasts is an important economic issue. The moreaccurate forecasts we get implies the more precise decisions we make. The central Bank reports inflation rates in certain periods of every year. In this reports the results ofinflation expectation survey are presented. In this study we use an approach in whichrelationship is determined by artificial neural network in high order fuzzy time seriesmodel. Time series of consumer price index is estimated by both the artificial neuralnetwork based method and some fuzzy approaches which is common in the literature.The results are compared to the results of inflation expectation survey analysisconducted by Central Bank of the Republic of Turkey in the aspect of forecastsaccuracy

Türkiye’de Enflasyonun İleri ve Geri Beslemeli Yapay Sinir Ağlarının Melez Yaklaşımı ile Öngörüsü

Obtaining the inflation prediction is an important problem. Having this prediction accurately will lead to more accurate decisions. Various time series techniques have been used in the literature for inflation prediction. Recently, Artificial Neural Network (ANN) is being preferred in the time series prediction problem due to its flexible modeling capacity. Artificial neural network can be applied easily to any time series since it does not require prior conditions such as a linear or curved specific model pattern, stationary and normal distribution. In this study, the predictions have been obtained using the feed forward and recurrent artificial neural network for the Consumer Price Index (CPI). A new combined forecast has been proposed based on ANN in which the ANN model predictions employed in analysis were used as data.Enflasyon öngörülerinin elde edilmesi önemli bir ekonomik problemdir. Öngörülerin doğru bir şekilde elde edilmesi daha doğru kararlara neden olacaktır. Enflasyon öngörüsü için literatürde çeşitli zaman serileri teknikleri kullanılmıştır. Son yıllarda zaman serisi öngörü probleminde esnek modelleme yeteneği nedeniyle, Yapay Sinir Ağları (YSA) tercih edilmektedir. Yapay sinir ağları doğrusal veya eğrisel belirli bir model kalıbı, durağanlık ve normal dağılım gibi ön koşullara ihtiyaç duymadığından herhangi bir zaman serisine kolaylıkla uygulanabilmektedir. Bu çalışmada Tüketici Fiyat Endeksi (TUFE) için ileri ve geri beslemeli yapay sinir ağları yaklaşımı kullanılarak öngörüler elde edilmiştir. Çözümlemede kullanılan YSA modellerinin öngörülerinin girdi olarak kullanıldığı, YSA’ya dayalı yeni bir melez yaklaşım önerilmiştir

Türkiye’de Enflasyonun İleri ve Geri Beslemeli Yapay Sinir Ağlarının Melez Yaklaşımı ile Öngörüsü

Obtaining the inflation prediction is an important problem. Having this prediction accurately will lead to more accurate decisions. Various time series techniques have been used in the literature for inflation prediction. Recently, Artificial Neural Network (ANN) is being preferred in the time series prediction problem due to its flexible modeling capacity. Artificial neural network can be applied easily to any time series since it does not require prior conditions such as a linear or curved specific model pattern, stationary and normal distribution. In this study, the predictions have been obtained using the feed forward and recurrent artificial neural network for the Consumer Price Index (CPI). A new combined forecast has been proposed based on ANN in which the ANN model predictions employed in analysis were used as data.Enflasyon öngörülerinin elde edilmesi önemli bir ekonomik problemdir. Öngörülerin doğru bir şekilde elde edilmesi daha doğru kararlara neden olacaktır. Enflasyon öngörüsü için literatürde çeşitli zaman serileri teknikleri kullanılmıştır. Son yıllarda zaman serisi öngörü probleminde esnek modelleme yeteneği nedeniyle, Yapay Sinir Ağları (YSA) tercih edilmektedir. Yapay sinir ağları doğrusal veya eğrisel belirli bir model kalıbı, durağanlık ve normal dağılım gibi ön koşullara ihtiyaç duymadığından herhangi bir zaman serisine kolaylıkla uygulanabilmektedir. Bu çalışmada Tüketici Fiyat Endeksi (TUFE) için ileri ve geri beslemeli yapay sinir ağları yaklaşımı kullanılarak öngörüler elde edilmiştir. Çözümlemede kullanılan YSA modellerinin öngörülerinin girdi olarak kullanıldığı, YSA’ya dayalı yeni bir melez yaklaşım önerilmiştir

Dendritic neuron model neural network trained by modified particle swarm optimization for time-series forecasting

Different types of artificial neural networks (NNs), such as nonprobabilistic and computation-based time-series forecasting tools, are widely and successfully used in the time-series literature. Whereas some of them use an additive aggregation function, others use a multiplicative aggregation function in the structure of their neuron models. In particular, recently proposed sigma-pi NNs and dendritic NNs have additional and multiplicative neuron models. This study aims to take advantage of the dendritic neuron model neural network (DNM-NN) in forecasting and hence uses the DNM-NN trained by a modified particle swarm optimization as the main contribution of the study optimization in time-series forecasting to improve the forecasting accuracy. To evaluate the forecasting performance of the DNM-NN, the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) was analyzed, and the obtained results were discussed together with the results produced by other time-series forecasting models, including traditional, fuzzy-based, and computational-based models

Multivariate intuitionistic fuzzy inference system for stock market prediction: The cases of Istanbul and Taiwan

Many of decision-making and policy planning processes involve a time-series prediction problem and so this area has extensive literature including a great variety of time-series prediction tools and inferences systems. An important part of these is based on fuzzy sets. However, it is known that fuzzy sets may fail to satisfy or characterize the uncertainty of the data in a comprehensive manner because they cannot depict the neutrality degree of time-series. Another important and decisive deficiency of current inference systems is to based on the univariate structure. However, the time series dealt with in a prediction problem generally interact with other time series. Considering these issues, creating an inference system based on intuitionistic fuzzy sets and multivariate relationships for a time series prediction problem is a requirement even an obligation. With these regards, this study presents a multivariate intuitionistic fuzzy time-series definition and its prediction models and introduces a multivariate intuitionistic fuzzy inference system (M-IFIS). The basic novelty of the article can be expressed as the definition of a multivariate intuitionistic fuzzy time series, as well as the creation of a relevant analysis mechanism, first-time in the literature. Sigma-pi neural network is used as an inference tool in M-IFIS and membership and non-membership values and lagged crisp observations of multivariable time-series are used as inputs of it. In order to reveal the performance of the proposed system, Istanbul Stock Exchange (IEX) and Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) are analysed and the results are evaluated as comprehensive and comparative. All findings reveal the superiority M-IFIS in predictive accuracy. © 202

Evaluation of the nutritional value of bee pollen by palynological, antioxidant, antimicrobial, and elemental characteristics

The object of this study was to characterize bee pollen (BP) as a food supplement according to its palynological, antioxidant, antimicrobial properties, and elemental contents. Twelve plant families, 35 genera, and one species were determined by palynological analysis of BP. Verbascum spp., Papaver spp., and Vicia spp. were found the major floral sources of BP. Two samples were determined as monofloral Verbascum spp. bee pollen. Total flavonoid (TFC) and phenolic content (TPC) varied from 117.5 to 142.09 mg QE/100 g, and 386.59 to 743.73 mg GAE/100 g, respectively. According to 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical and 2,2\"-azino-bis3-ethylbenzothiazoline-6-sulfonic acid (ABTS) radical cation assays, the BP samples demonstrated high antioxidant activity. Result of ferric reducing antioxidant power (FRAP) and metal chelating activities (MCA) assays were ranging from 61.17 to 69.7% and 74.99 to 87.78%. Antimicrobial activities of the BP were determined by the agar well diffusion and microplate method. Obtained results indicated that BP showed appreciable antibacterial activity against Escherichia coli, Bacillus cereus, and Staphylococcus aureus strains by remarkably decreasing bacterial growth. Thirty-one elements were analyzed in BP samples by inductively coupled plasma-mass spectrometry (ICP-MS). Target hazard quotients (THQ), hazard index (HI), and estimated daily intake (EDI) values were calculated using selected elements\" results. Considering these values, it was determined that the consumption of bee pollen was safe for adults and children. BP samples can be used as a food supplement because of their high antioxidant and antimicrobial capacity and elemental content

A novel intuitionistic fuzzy time series prediction model with cascaded structure for financial time series

© 2022 Elsevier LtdFinancial time series prediction problems, for decision-makers, are always crucial as they have a wide range of applications in the public and private sectors. This study presents a cascaded intuitionistic fuzzy model for financial time series prediction. The proposed prediction model has the ability to jointly and simultaneously model linear and nonlinear relationships in financial time series. Thus, it can adapt itself to both linear and non-linear surfaces of the data and can produce satisfactory predictions for financial time series. Moreover, the other reason why to be produced better predictions, the proposed model reckons non-membership degrees in addition to membership degrees in the prediction process. With these aspects, the proposed prediction model is different and superior to all models in the literature. This superiority has been proven by the analysis of 48 different financial time series containing TAIEX, DIJ, SSEC, and IEX data sets. The results have been evaluated in terms of RMSE, MAPE, and MdRAE metrics and some other perspectives as well. The proposed prediction model has achieved progress in prediction performance, up to 80% for TAIEX 2000–2004 datasets, 60% for TAIEX 2008–2018 datasets, approximately 50% for DJI and SSEC, and up to 70% for IEX. All the discussed indicators demonstrated the outstanding prediction performance of the proposed cascaded intuitionistic prediction model compared to some other state-of-the-art prediction tools