Complexification methods of interval forecast estimates in the problems on short­term prediction

Romanenkov, Yuri; Danova, Mariia; Kashcheyeva, Valentyna; Bugaienko, Oleg; Volk, Maksym; Karminska-Bielobrova, Maryna; Lobach, Olena

doi:10.15587/1729-4061.2018.131939

Cited by 3 publications

(6 citation statements)

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“…The prerequisites for the formalization of expert assessments in interval form may be the following factors [46]:…”

Section: Prerequisites For the Formalization Of Expert Data In Interv...mentioning

confidence: 99%

“…The formalization option proposed in [46,54] was used to determine the quantitative measure of the proximity of interval errors to zero. This choice is subjective and due to the wide possibilities of parametric tuning of the model in this case.…”

Section: Construction Of Models Of Adaptive Integration Of Weighted I...mentioning

confidence: 99%

“…According to [46], the quantitative measure of the proximity of the interval error Δ Δ Δ [ ]=     , to zero can be determined as follows:…”

Section: Construction Of Models Of Adaptive Integration Of Weighted I...mentioning

confidence: 99%

“…As this has a direct impact on the accuracy of the final estimates, subsequent seminal papers may investigate this issue. This can serve as a development of the proposed models in terms of estimating the upper limit of adequacy, as well as a whole group of models reported, for example, in [8,20,21,30,46].…”

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Models of adaptive integration of weighted interval data in tasks of predictive expert assessment

Рубан

Horenskyi

Romanenkov

et al. 2022

EEJET

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The problem of improving the methodological and algorithmic support of the complexing process by developing models of adaptive redundant complexing of weighted interval data has been solved. The object of this study is the process of complexing interval data obtained from several independent sources; the subject is the algebraic methods of excessive complexing of weighted interval data. The relevance of the task is due to the severity of the problem of consolidating homogeneous data in order to obtain more accurate and relevant information about the object or process under study. Models have been developed that, unlike the existing ones, make it possible a posteriori to take into account the accuracy of experts at the preliminary stage of expert evaluation. A single analytical form of the model for processing weighted interval and point estimates with the possibility of structural and parametric tuning is proposed. It allows one to increase the degree of automation of processing expert assessments under conditions of interval uncertainty. Recommendations for the practical application of the proposed models have been formulated. Options for parametric configuration of preference functions were indicated depending on the characteristics of weighted interval estimates. The commonality of the limiting cases of the proposed models with previously known ones is proved. The example shows the shift of the integrated assessment to the side of more accurate assessments at the previous stage of source assessment. The adaptability of the proposed models is illustrated. At the same time, a slight, on average, about 10 %, expansion of the complexed interval relative to the primary ones was registered. The built models and algorithms can be used in automated expert systems, as well as in cascade models of information processing and compression.

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“…The prerequisites for the formalization of expert assessments in interval form may be the following factors [46]:…”

Section: Prerequisites For the Formalization Of Expert Data In Interv...mentioning

confidence: 99%

Section: Construction Of Models Of Adaptive Integration Of Weighted I...mentioning

confidence: 99%

“…According to [46], the quantitative measure of the proximity of the interval error Δ Δ Δ [ ]=     , to zero can be determined as follows:…”

Section: Construction Of Models Of Adaptive Integration Of Weighted I...mentioning

confidence: 99%

mentioning

confidence: 99%

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Models of adaptive integration of weighted interval data in tasks of predictive expert assessment

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Horenskyi

Romanenkov

et al. 2022

EEJET

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“…At the same time, the relevance of combined or hybrid forecasting methods, as well as methods for complexing forecast ratings [5], increases. It should also be taken into account that the primary data in many cases are characterized by various kinds of uncertainty, which necessitates the adaptation of traditional methods of analysis to such features [6].…”

Section: Introductionmentioning

confidence: 99%

Researching Priority Directions in the Area of Data Mining

Ruban¹,

Ilina²,

Mozhaiev³

2020

CNCS

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Developing a risk management approach based on reinforcement training in the formation of an investment portfolio

Martovytskyi

Argunov

Рубан

et al. 2023

EEJET

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Investments play a significant role in the functioning and development of the economy. Risk management is an integral part of the formation of the investment portfolio. This means that an investor must be willing to take on a certain level of risk in order to receive a certain level of return. However, when forming an investment portfolio, an investor faces such problems as market unpredictability, asset correlation, incorrect asset allocation. Therefore, when forming an investment portfolio, an investor should carefully study all possible risks and try to minimize them. The object of research is an approach to risk management in the formation of an investment portfolio using the method of reinforcement training. The basic principles of formation of the investment portfolio and determination of risks are described. The application of the method of reinforcement training for building a model of risk management of investment portfolio is considered. The process of selecting optimal investment assets based on alternative data sources that minimize risks and maximize profits is also considered. A functional model of the process of risk optimization in the formation of an investment portfolio based on machine learning methods has been developed. The functional model constructed makes it possible to build a process of risk optimization, including asset selection, risk comparison and assessment, to form an investment portfolio and monitor its risks. The study results showed that the proposed approach to the formation of the investment portfolio increased the total growth of the investment portfolio by 0.4363 compared to the base model. Also, the volatility indicator improved compared to the market, as evidenced by the percentage difference between the initial and final cash amount, which increased from 128.98 to 295.57

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Complexification methods of interval forecast estimates in the problems on shortterm prediction

Cited by 3 publications

References 5 publications

Models of adaptive integration of weighted interval data in tasks of predictive expert assessment

Models of adaptive integration of weighted interval data in tasks of predictive expert assessment

Researching Priority Directions in the Area of Data Mining

Developing a risk management approach based on reinforcement training in the formation of an investment portfolio

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Complexification methods of interval forecast estimates in the problems on short­term prediction

Cited by 3 publications

References 5 publications

Models of adaptive integration of weighted interval data in tasks of predictive expert assessment

Models of adaptive integration of weighted interval data in tasks of predictive expert assessment

Researching Priority Directions in the Area of Data Mining

Developing a risk management approach based on reinforcement training in the formation of an investment portfolio

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Complexification methods of interval forecast estimates in the problems on shortterm prediction