часовий ряд

Введено структуру адаптивного нео-фаззі-предиктора та багатовимірного нео- фаззі-нейрона, а також метод навчання останнього. Запропонований алгоритм навчання має підвищену швидкість збіжності та забезпечує фільтруючі властивості. Завдяки введеній нейромережевій архітектурі, вузлами якої є нео-фаззі-нейрони, можна розв’язувати задачі короткострокового прогнозування у реальному часі за умов короткої навчальної вибірки.

Досліджено вплив функції активації нейронної мережі типу RBF на ефективність навчання та прогнозування надійності програмного забезпечення у вигляді часових рядів. Показано, що оптимальною функцією активації для цієї задачі є Inverse Multiquadric з 10 нейронами у вхідному шарі та 30 – у прихованому.

Predicting a building’s energy consumption plays an important role as it can help assess its energy efficiency, identify and diagnose energy system faults, and reduce costs and improve climate impact. An analysis of current research in the field of ensuring the energy efficiency of buildings, in particular, their energy assessment, considering the types of models under consideration, was carried out.

The study analyzes the coordinate time series of five permanent International GNSS Service (IGS) stations located in New Zealand. It also considers their annual movement from 2009 to 2018. The raw data in the form of Receiver Independence Exchange (RINEX) files were taken from IGS database and processes by means of online processing service AUSPOS. Using coordinate time series, horizontal and vertical displacement rates were calculated covering the ten-year study period. According to the results, stations located at the North Island of New Zealand revealed an uplift of 31-32 mm/yr.

The purpose of time series modelling is to predict future indicators based on the study and analysis of past and present data. Various time series methods are used for forecasting. The article uses econometric extrapolation research methods. Analyzed scientific works are related to extrapolation methods for forecasting time series. The dynamics of the financial formation related to results of Ukrainian insurance companies by the types of their activities have been analyzed. The main factors that determine the effectiveness are determined.

The study is devoted to the development of information technology for forecasting based on time series. It has been found that it is important to develop new models and forecasting methods to improve the quality of the forecast. Information technology is based on the evolutionary method of synthesis of the forecast scheme grounded on basic forecast models. The selected method allows you to consider any number of predictive models that may belong to different classes.

This paper is devoted develop software for time series forecasting using Python programming language. SARIMA model was used to develop the system.

The method of construction of the combined model of forecast ing of time series based on basic models of forecasting is developed in the work. The set of basic models is dynamic, ie new prediction models can be included in this set. Models also can be deleted depending on the properties of the time series. For the synthesis of a combined model of forecasting time series with a given forecast step, the optimal step of prehistory is determined at the beginning. Next the functional is constructed.

The article addresses the problem of the restoration of missed values in the results of testing the recipients given by the time series. As experimental data, the time series with spaces are given. Recovery efficiency is estimated by the relative error of the recovered value. Examples of restoration missing data in the time series table and the individual time series are given. Used simple methods for replacing missed value by average, weighted average and median.

Analysis of the debris flow formation factors which cause the long-term activity of debris flows is made. The methodology of the debris flows prediction subject to meteorological, hydrological, seismic, heliophysical factors is proposed. The regularities of long-term seasonality of these factors by using autocorrelation and spectral analysis are revealed. The integral rate of probability of debris flow intensification is calculated. The time series of this integral rate is extrapolated and the following peak of debris flows activation is predicted.