ARIMA

MATHEMATICAL MODELS FOR THE ANALYSIS AND FORECASTING OF RIVER WATER POLLUTION USING THE MULTIFRACTAL METHOD

This paper explores multifractal analysis for the selected time series water pollution data set and further prediction based on BOD measure with ARFIMA-based fractal model. MFDFA multifractal algorithm is applied for estimating the fractal differentiation parameter of the ARFIMA. The obtained results are compared with similar obtained with autoregressive ARIMA model and basic ARFIMA fractal model. The study reveals an enhancement in accuracy with the use of combination of multifractal analysis and fractal methods for water pollution prediction

MODELS FOR TIME SERIES FORECASTING USING ARIMA AND LSTM IN ECONOMICS AND FINANCE

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.

MIGRATION OF SERVICES IN A KUBERNETES CLUSTER BASED ON WORKLOAD FORECASTING

The article delves into the intricate challenge of scaling microservices within a Kubernetes cluster, thoroughly examining existing methodologies for scaling microservice architectures, and presenting a novel approach that involves migrating specific components. Unlike the conventional horizontal and vertical scaling strategies, which require additional resources, this proposed method focuses on migrating non-critical components to another Kubernetes cluster.

DEVELOPMENT OF SOFTWARE AND ALGORITHMIC EQUIPMENT FOR PREDICTION OF RIVER WATER POLLUTION USING FRACTAL ANALYSIS METHODS

This paper explores the application of the ARFIMA fractal model for prediction of the dynamics of river water pollution based on BOD measure. The study begins by conducting a review of related works in the field of water quality analysis. At this stage also a suitable dataset is selected, that is used to train the ARFIMA, one of the machine learning models. GPH semi-parametric algorithm is applied for estimating the fractal differentiation parameter of the ARFIMA.

Forecasting network requests numbers to cloud application

The article contains analysis of existing time series forecast methods. It is estimated how these methods fit to time series of network requests to a cloud application. Optimal forecasting methods have been chosen for different working modes of a cloud application. The research also contains comparison of forecast performed by standard methods and developed combined method.