Temirkhanov A., Костромина А. М., Цымбой О. А. et al., Доклады Российской академии наук. Математика, информатика, процессы управления (ранее - Доклады Академии Наук. Математика) 2025 Т. 527 № S С. 485–494
The industry is rich in cases when we are required to make forecasting for large amounts of time series at once. However, we might be in a situation where we can not afford to train a separate model for each of them. Such issue in time series modeling remains without due attention. The remedy for ...
Added: February 24, 2026
Manevich V., Ignatov D. I., Applied Soft Computing Journal 2025
This paper studies the forecasting of time series of exchange assets values and realized volatility. We take their most prominent representatives from cryptocurrencies (3 assets) and the largest participants of the S\&P500, divided into 12 sets by sectors (86 assets). The paper compares a large number (above 200000) of the state-of-the-art models starting from classical ...
Added: September 19, 2024
Pankratova Y., Trofimova I., Firyago U. et al., Lecture Notes in Networks and Systems 2023 Vol. 596 P. 277–287
In this paper, models for forecasting the dynamics of demand for products with a short expiration date are constructed. Here we propose to construct models for time series forecasting using the decomposition method and taking into account the assumptions of experts about the influence of certain factors on the behavior of product consumers. These models ...
Added: March 20, 2024
Bufalo M., Orlando G., Tourism Review 2024 Vol. 79 No. 2 P. 445–464
This study aims to predict overnight stays in Italy at tourist accommodation facilities through a nonlinear, single factor, stochastic model called CIR#. The contribution of this study is twofold: in terms of forecast accuracy and in terms of parsimony (both from the perspective of the data and the complexity of the modeling), especially when a ...
Added: February 16, 2024
Moreido V., Gartsman B., Solomatine D. P. et al., Water (Switzerland) 2021 Vol. 13 No. 12 Article 1696
With more machine learning methods being involved in social and environmental research activities, we are addressing the role of available information for model training in model performance. We tested the abilities of several machine learning models for short-term hydrological forecasting by inferring linkages with all available predictors or only with those pre-selected by a hydrologist. ...
Added: January 31, 2023
Sizykh N., Orshanskaya E., Sizykh D., , in: 2022 15th International Conference Management of large-scale system development (MLSD).: M.: IEEE, 2022. P. 1–6.
The paper presents the research results of the predictive ability of stock quote forecasting models using the ARIMA/LSTM hybrid model. This study is based on a predictive power analysis using a sample of 30 companies from three sectors: energy, finance, and technology. ...
Added: November 14, 2022
Trudaeva T. A., Медведкова И. В., Красникова С. А. et al., ScienceDirect, 2021.
This volume contains the papers presented at BICA*AI 2020: the 2020 Annual
International Conference on Brain-Inspired Cognitive Architectures for Artificial
Intelligence, also known as the Eleventh Annual Meeting of the BICA Society,
held virtually on November 10-15, 2020 in Natal, Rio Grande do Norte.
Biologically Inspired Cognitive Architectures (BICA) are computational frameworks for building intelligent agents that are inspired ...
Added: June 15, 2022
Danilov K., Барахнин В. Б., , in: INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS 2021 SPECIAL INTEREST GROUP ON BIG DATA PROCEEDINGS.: [б.и.], 2022.
Big data is the foundation of modern energy management systems. There are two energy consumption models where systems are one of the consumers with intelligent equipment: static and dynamic. The dynamic model uses a two-tariff closed-loop accounting scheme, which implies changes in tariffs based on the analysis of current consumption. The results of an experimental ...
Added: June 13, 2022
Danilov K., Maltseva S. V., Информационные технологии 2021 Т. 27 № 10 С. 550–560
The automated feature engineering method in the problem of forecasting energy consumption is considered. The algorithm of the method and the scheme of the forecasting model construction are stated. The proposed approach was tested on data about electricity consumption in Russian regions. The results of the computational experiments carried out using the described method demonstrate ...
Added: December 3, 2021
Moiseev N., Sorokin A., Zvezdina N. et al., Mathematics 2021 No. 9(19) Article 2423
The research paper is devoted to developing a mathematical approach for dealing with time-varying parameters in rolling window logit models for credit risk assessment. Forecasting coefficients yields a better model accuracy than a trivial approach of using computed past statistics parameters for the next time period. In this paper, a new method of dealing with ...
Added: October 1, 2021
Petrovskiy M., Korolev V., Korchagin A. et al., Programming and Computer Software 2018 Vol. 44 No. 5 P. 353–362
Added: December 5, 2018
Меланко А. Г., Аудит и финансовый анализ 2016 № 6 С. 337–346
This article describes current state of container shipping market and considers factors affected on container shipping demand including significant impact of macro environment factors. Approaches of demand forecasting in container shipping market were examinedfor both Russia and other countries. A new hybrid model, underpinned by the conducted analysis, was proposed. This model is a modification ...
Added: March 12, 2017