1,721,050 research outputs found
Dendritic neuron model neural network trained by modified particle swarm optimization for time-series forecasting
No full textDifferent 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
Forecasting inflation rates with high order fuzzy ime series approach
Get PDFEnflasyon öngörülerinin elde edilmesi önemli bir ekonomik problemdir. Öngörülerin daha do ru elde edilmesi daha do ru kararlara neden olacakt r. T.C. Merkez bankas her y l n belirli dönemlerinde enflasyon raporlar yay nlamaktad r. Raporlarda enflasyon beklentisi anketi sonuçlar yer almaktad r. Bu çal mada tüketici fiyat endeksi yüksek dereceli bulan k zaman serisi yakla m ile öngörülmü tür. Yüksek dereceli bulan k zaman serisi modelinde ili kilerin belirlenmesi yapay sinir a lar ile yap lmaktad r. Tüketici fiyat endeksi zaman serisi, ayr ca literatürde yer alan baz bulan k zaman serisi yakla mlar ile tahmin edilerek, öngörü do rulu u aç s ndan T.C. Merkez Bankas enflasyon beklentisi anketi sonuçlar ile kar la t r lm t rTo obtain inflation forecasts is an important economic issue. The more accurate 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 of inflation expectation survey are presented. In this study we use an approach in which relationship is determined by artificial neural network in high order fuzzy time series model. Time series of consumer price index is estimated by both the artificial neural network based method and some fuzzy approaches which is common in the literature. The results are compared to the results of inflation expectation survey analysis conducted by Central Bank of the Republic of Turkey in the aspect of forecasts accurac
The Forecasting Of Labour Force Participation And The Unemployment Rate In Poland And Turkey Using Fuzzy Time Series Methods
Get PDFFuzzy 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
A robust training of dendritic neuron model neural network for time series prediction
No full text© 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.Many prediction methods proposed in the literature can be concerned under two main headings: probabilistic and non-probabilistic methods. In particular, as a kind of non-probabilistic model, artificial neural networks (ANNs), having different properties, have been commonly and effectively used in the literature. Some ANNs operate the additive aggregation function in the structure of their neuron models, while others employ the multiplicative aggregation function. Recently proposed dendritic neural networks also have both additional and multiplicative neuron models. The prediction performance of such an artificial neural network will inevitably be negatively affected by the outliers that the time series of interest may contain due to the neuron model in its structure. This study, for the training of a dendritic neural network, presents a robust learning algorithm. The presented robust algorithm is the first for the training of DNM in the literature as far as is known and uses Huber\"s loss function as the fitness function. The iterative process of the robust learning algorithm is carried out by particle swarm optimization. The productivity and efficiency of the suggested learning algorithm were evaluated by analysing different real-life time series. All analyses were performed with original and contaminated data sets under different scenarios. The R-DNM has the best performance for the original data sets with a value of 2.95% in the ABC time series, while the FTSE showed the best performance in approximately 27% and the second best in 33% of all analyses. The proposed R-DNM has been the least affected by outliers in almost all scenarios for contaminated ABC data sets. Moreover, it has been the least affected model by outliers in approximately 71% of the 90 analyses performed for the contaminated FTSE time series. The obtained results show that the dendritic artificial neural network trained by the proposed robust learning algorithm produces the satisfactory predictive results in the analysis of time series with and without outliers
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Evaluation of the nutritional value of bee pollen by palynological, antioxidant, antimicrobial, and elemental characteristics
No full textThe 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 New Robust Regression Method Based on Particle Swarm Optimization
No full textRegression analysis is one of methods widely used in prediction problems. Although there are many methods used for parameter estimation in regression analysis, ordinary least squares (OLS) technique is the most commonly used one among them. However, this technique is highly sensitive to outlier observation. Therefore, in literature, robust techniques are suggested when data set includes outlier observation. Besides, in prediction a problem, using the techniques that reduce the effectiveness of outlier and using the median as a target function rather than an error mean will be more successful in modeling these kinds of data. In this study, a new parameter estimation method using the median of absolute rate obtained by division of the difference between observation values and predicted values by the observation value and based on particle swarm optimization was proposed. The performance of the proposed method was evaluated with a simulation study by comparing it with OLS and some other robust methods in the literature
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