Short-Term Fuzzy Forecasting of Brent Oil Prices

Ismagilov, I. I.; Khasanova, S. F.

doi:10.5539/ass.v11n11p60

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Also, the other methods, basing on wavelet decomposition, neural networks, etc. usually omit influence of other factor and focus on a single time-series [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61]. These makes DMA an interesting method for a practitioner.…”

Section: Economic Motivationmentioning

confidence: 99%

Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R

Drachal

2020

Signals

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The described R package allows to estimate Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model. The original methods, and additionally, some selected modifications of these methods are implemented. For example the user can choose between recursive moment estimation and exponentially moving average for variance updating in the base DMA. Moreover, inclusion probabilities can be computed in a way using “Google Trends” data. The code is written with respect to minimise the computational burden, which is quite an obstacle for DMA algorithm if numerous variables are used. For example, this package allows for parallel computations and implementation of the Occam’s window approach. However, clarity and readability of the code, and possibility for an R-familiar user to make his or her own small modifications in reasonably small time and with low effort are also taken under consideration. Except that, some alternative (benchmark) forecasts can also be quickly performed within this package. Indeed, this package is designed in a way that is hoped to be especially useful for practitioners and researchers in economics and finance.

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Section: Economic Motivationmentioning

confidence: 99%

Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R

Drachal

2020

Signals

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“…Therefore, at present, artificial intelligence methods are used when solving many problems related to the assessment of the state of objects [5][6][7][8][9][10]. The theory of fuzzy sets [11][12][13][14][15], as one of the directions of artificial intelligence, enables to construct fuzzy models of objects. Methods of fuzzy logic are widely used to solve problems of diagnostics, control, support for decision-making in various subject areas [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Neural Network Model for Rules Generating of the Objects State Determining in Uncertainty

Ismagilov¹

2018

helix

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This article solves the problem of modelling a fuzzy neural network to generate fuzzy rules and to use them for determining the state of objects in conditions of uncertainty. Traditional methods of mathematical statistics do not allow constructing adequate models of objects under the specified conditions. Therefore, at present, the solution of many problems is based on the use of data mining technologies using the methods of fuzzy logic. The traditional approach to the construction of fuzzy systems is associated with the need to involve an expert to formulate fuzzy rules. To eliminate this drawback, the automation of generating fuzzy rules based on machine learning methods is relevant. One of the approaches to solving this problem is to build a fuzzy neural network and instruction in it based on the data characterizing the object under study. The implementation of this approach required the choice of the type of fuzzy rules, taking into account the peculiarities of the processed data. In addition, the development of an algorithm of logic rule-based inference was required. The stages of the algorithm determine the number of layers in a fuzzy neural network and their functionality. An algorithm for learning a fuzzy neural network has been developed, as a result of which a system of fuzzy-production rules is built. In order to assess the effectiveness of the developed model of a fuzzy neural network, a software package has been implemented. On its basis, the studies on assessment of the classifying ability of the generated rules using the example of data analysis from the UCI Machine Learning Repository have been conducted. The research results show that the classifying ability of fuzzy rules is not inferior to other classification methods in accuracy. In addition, the inferencing algorithm based on the rules makes it possible to successfully perform the classification in the absence of some of the source data. This proves the significance of the obtained results and the effectiveness of the proposed approach for solving the problem of instance state estimate in conditions of fuzziness, heterogeneity and the lack of a part of the analyzed data.

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“…In addition, the use of such methods is complicated by a number of circumstances, one of which is the possible inaccuracy and vagueness of the raw information about the object being assessed. To analyze the objects and phenomena in the indicated conditions, nowadays, the methods of expert estimation [12] and the theory of fuzzy sets [13][14][15][16] are being actively used, which makes their application relevant for solving the problem of data security risk assessment [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Security Risk Assessment Method Using Fuzzy Logic

Ismagilov¹

2018

helix

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The article solves the problem of assessing security risks in information systems using the methods of the theory of fuzzy sets. The urgency of solving this problem is determined by the complexity of applying a number of existing methods, the possible inaccuracy of quantitative assessments of risk factors, and the possible inadequacy, uncertainty and quality of the initial information. The paper carries out formalization of the subject area "Information Security Risk" in the form of a conceptual model within the framework of the types of ER diagram, and determines the semantics of its concepts within the framework of the theory of categories and factors. The developed methods for assessing information security risks and evaluating the effectiveness of countermeasures are capable of solving problems in the indicated conditions. We have conducted the experiments on the application of the technique on a specific object of protection. The set of countermeasures recommended for implementation shows high efficiency in terms of absolute risk reduction. Recommendations on the choice of forms of membership functions of fuzzy scales used in risk assessment, as well as recommendations on the choice of fuzzy operations when performing calculations are given. The practical application of the developed methodology has a high practical value for building effective information protection systems in terms of expected damage.

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Short-Term Fuzzy Forecasting of Brent Oil Prices

Cited by 4 publications

References 26 publications

Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R

Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R

Fuzzy Neural Network Model for Rules Generating of the Objects State Determining in Uncertainty

Security Risk Assessment Method Using Fuzzy Logic

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