Abstract
The objective of this work is to develop an intelligent ensemble forecasting system. The task of forecasting is to assess the trend of a particular indicator. Using various forecasting methods, we can obtain predictions of the future values of a selected indicator. Scholars and practitioners often face the problem of choosing the best forecasting method. A combination of independent individual forecasts offers a solution to this problem. In this article, the models and methods for financial time series forecasting and periods of speculative growth in the stock markets identifying are considered. The article proposes a concept of an intelligent forecasting system and presents a software product called MultFinance, which implements both artificial intelligence methods and statistical methods. Multifinance software complex is used for forecasting of micro - and macro-economic indicators as well as in forecasting of the securities, currency exchange and commodity market parameters. Due to the fact that the neural analysis unit is implemented in the system, the system is able to generate a precise forecast in time of crisis. Multiple factor analysis is also represented in the MultFinance system, which has a positive impact on the forecast efficiency and performance. The MultFinance system includes a unit for identifying periods of speculative growth, which is implemented on the basis of a perceptron. It can be used to assess the risk of assets and determine the number of financial bubbles in the financial market. #COMESYSO1120
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Ivanyuk, V., Sunchalin, A., Sunchalina, A. (2020). Development of an Intelligent Ensemble Forecasting System. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Software Engineering Perspectives in Intelligent Systems. CoMeSySo 2020. Advances in Intelligent Systems and Computing, vol 1294. Springer, Cham. https://doi.org/10.1007/978-3-030-63322-6_40
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DOI: https://doi.org/10.1007/978-3-030-63322-6_40
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