The article investigates the limitations of modern approaches to time series forecasting in complex dynamic and nonlinear processes whose structure may consist of heterogeneous data types. The relevance of the study is driven by the rapid growth of data volumes in information systems, their diversity, and the need to improve forecasting accuracy under conditions of non-stationarity and multifactor influence.
The article considers the problem of forecasting extreme temperature phenomena as one of the key components of ensuring the stability of the functioning of modern natural-technogenic and socio-economic systems in the face of climate change. The relevance of the study is due to the increase in the frequency and intensity of abnormally high and low temperatures, which cause significant risks to energy infrastructure, transport systems, the agro-industrial complex and public safety.
The article considers modern approaches to modeling financial flows of domestic enterprises engaged in foreign economic activity during the economic crisis caused by military actions in Ukraine. In wartime, enterprises face significant difficulties in managing financial flows due to the instability of the economic environment, and, accordingly, to overcome these challenges, it is necessary to develop adaptive strategies that will ensure the stability of financial flows.
The article explores approaches to forecasting the development trends of the IT market using machine learning methods. The relevance of the research is driven by the high dynamics of the digital economy, rapid technological changes, and the need for scientifically grounded analytical tools in the IT domain. The purpose of the study is to develop a forecasting model capable of identifying patterns in socio-economic, technological, and behavioral indicators that determine the state and prospects of IT market development.
The article addresses challenges related to climate change research. It provides an overview of contemporary information technologies that facilitate the design of highly efficient climate monitoring information systems, specifically in terms of processing speed and data completeness.
Extreme rainfall in upstream watersheds often results in the rise of river water levels, leading to severe flood disasters in the downstream catchment. Therefore, monitoring river water levels and flow is crucial for flood forecasting in early warning systems and disaster risk reduction.
Time series forecasting is a crucial task in economics, business, and finance. Traditionally, forecasting methods such as autoregression (AR), moving average (MA), exponential smoothing (SES), and, most commonly, the autoregressive integrated moving average (ARIMA) model are used. The ARIMA model has demonstrated high accuracy in predicting future time series values. With the advancement of computational power and deep learning algorithms, new approaches to forecasting have emerged.
Nowadays, neural networks are among the most popular analysis tools. They are effective in solving classification, pattern recognition, and clustering problems. This paper provides a detailed description and analysis of the operational principles of two neural networks, namely a Siamese network and a multilayer perceptron. A model for using these neural networks in time series forecasting is proposed. As an example, a web application was created in which the described neural networks were used to analyze the correlation between pairs of financial assets and assess t
The study substantiates the feasibility of using machine learning technology to improve the iteration planning process in IT projects implemented using the Scrum methodology. The problem of productivity planning in teams is set. The subject and object of the research are formulated. The expected scientific novelty and practical significance of the research results are described. A range of potential issues related to task planning in IT projects, particularly the accuracy of team productivity forecasting, is considered.
The field of time series forecasting has grown significantly over the past several years and is now highly active. In numerous application domains, deep neural networks are exact and powerful. They are among the most popular machine learning techniques for resolving big data issues because of these factors. Historically, there have been numerous methods for accurately predicting the subsequent change in time series data. The time series forecasting problem and its mathematical underpinnings are first articulated in this study. Following that, a description of the m