Selection of time series forecasting model, using a combination of linguistic and numerical criteria

Afanasieva, T. V.; Sapunkov, A.

doi:10.1109/icaict.2016.7991715

Cited by 7 publications

(1 citation statement)

References 9 publications

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“…4), а также представлен прогноз на основе метода наименьших квадратов. Соответствующие расчеты произведены стандартными средствами MS Excel «Построение линии тренда (аппроксимация и сглаживание)», но они могут быть выполнены и другими средствами [12,13]. В данной работе предложен слайс-прогноз по восьми показателям (рис.…”

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Slice forecast and its accuracy criterion

KARPUKHIN

2021

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Subject. This article provides a mathematical formulation of a slice-based forecast technique allowing a comprehensive assessment of future changes in the dynamics and structure of economic systems. The technique is based on an analysis and integration of a set of time series of heterogeneous indicators combined in a system logical algorithm of information synthesis called a slice. A slice forecast accuracy criterion is proposed as well. Objectives. Slice forecasts are designed to improve the quality and efficiency of economic forecasts. Methods. The slice forecast technique is based on a slice technology as a set of methods to collect, process, analyze, and synthesize information and knowledge. Results. The article presents a calculation based on eight series of macroeconomic indicators that characterize the development of the economy of the Russian Federation for the period from 2000 to 2021. It shows new possibilities of analysis and description of economic systems, cycles and crisis phenomena. Conclusions. The results obtained show that the slice technique helps solve a number of urgent problems to improve the quality of foreseeing future changes.

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Slice forecast and its accuracy criterion

KARPUKHIN

2021

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Common Mutual Information Selection Algorithm and Its Application on Combination Forecasting

Shen,

Chen

2024

Journal of Forecasting

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The subset selection of individual prediction methods is gradually becoming a hot topic. Among numerous forecasts, identifying the optimal subset approach has become a major focal point of research. To address this issue, the paper introduces a novel method based on information theory, which is called common mutual information (CMI) selection algorithm. This optimal subset selection method not only simultaneously considers the relationships of three factors, which include the candidate feature set, the selected feature set, and the actual time series, but also provides a more precise treatment of these relationships. Therefore, CMI algorithm employs the mutual information (MI) shared among the three factors as the criterion for selection and improves the accuracy of the redundancy or correlation measure for existing algorithms. Furthermore, it overcomes the deficiency of calculating MI between the candidate subset and the actual time series. Existing algorithms use the average MI values between individual elements within the subset and the actual sequence; this paper takes the selected subset as a multidimensional input for MI computation, thus reducing computational errors. Finally, the proposed algorithm is compared with two other approaches of the MI algorithm, the Max‐Relevance and Min‐Redundancy (mRMR) algorithm in both theoretical and empirical aspects. The experiments are illustrated to show the effectiveness and superiority of CMI algorithm.

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Assessing the Software Developer’s Quality Using Fuzzy Estimates

Afanasieva

Моисеев

2018

Advances in Intelligent Systems and Computing

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Selection of time series forecasting model, using a combination of linguistic and numerical criteria

Cited by 7 publications

References 9 publications

Slice forecast and its accuracy criterion

Slice forecast and its accuracy criterion

Common Mutual Information Selection Algorithm and Its Application on Combination Forecasting

Assessing the Software Developer’s Quality Using Fuzzy Estimates

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