scholarly journals Short-term forecasting models for automated data backup system: segmented regression analysis

2020 ◽  
Vol 42 ◽  
pp. e46073
Author(s):  
Leandro Duarte Pereira ◽  
Pedro Paulo Balestrassi ◽  
Vinicius de Carvalho Paes ◽  
Anderson Paulo de Paiva ◽  
Rogério Santana Peruchi ◽  
...  
Keyword(s):  
Linear Regression ◽  
Control Charts ◽  
Business Success ◽  
Short Term ◽  
Data Backup ◽  
Backup System ◽  
Logical Sequence ◽  
Forecast Models ◽  
Short Term Forecasting ◽  
Object Of Study

The Information and Communication Technology (ICT) becomes a critical area to business success; organizations need to adopt additional measures to ensure the availability of their services. However, such services are often not planned, analyzed and monitored, which impacts the assurance quality to customers. The backup is the service addressed in this study, with the object of study of the automated data backup systems in operation at the Federal University of Itajuba - Brazil. The main objective of this research was to present a logical sequence of steps to obtain short-term forecast models that estimate the point at which each recording media reaches its storage capacity limit. The input data was collected in the metadata generated by the backup system, with 2 years data window. For the implementation of the models, the simple univariate linear regression technique was employed in conjunction, in some cases, with the simple segmented linear regression. In order to discover the breakpoint, a targeted approach to residual analysis was applied. The results obtained by the iterative implementation of the proposed algorithm showed adherence to the characteristics of the analyzed series, with accuracy measures, regression significance, normality residual through control charts, model adjustment, among others. As a result, an algorithm was developed for integration into automated backup systems using the methodology described in this study.

scholarly journals Application of time series models for heating degree day forecasting

2020 ◽  
Vol 12 (1) ◽  
pp. 2137-2146
Author(s):  
Merve Kuru ◽  
Gulben Calis
Keyword(s):  
Time Series ◽  
Information Criteria ◽  
Percentage Error ◽  
Short Term ◽  
Absolute Deviation ◽  
Degree Day ◽  
Forecast Models ◽  
Time Series Analyses ◽  
Short Term Forecasting ◽  
Sarima Models

AbstractThis study aims at constructing short-term forecast models by analyzing the patterns of the heating degree day (HDD). In this context, two different time series analyses, namely the decomposition and Box–Jenkins methods, were conducted. The monthly HDD data in France between 1974 and 2017 were used for analyses. The multiplicative model and 79 SARIMA models were constructed by the decomposition and Box–Jenkins method, respectively. The performance of the SARIMA models was assessed by the adjusted R2 value, residual sum of squares, the Akaike Information Criteria, the Schwarz Information Criteria, and the analysis of the residuals. Moreover, the mean absolute percentage error, mean absolute deviation, and mean squared deviation values were calculated to evaluate the performance of both methods. The results show that the decomposition method yields more acceptable forecasts than the Box–Jenkins method for supporting short-term forecasting of the HDD.

EFFECT OF CHANGES IN THE LEVEL DURING THE YEAR ON THE SLOPE OF THE WATER SURFACE OF THE MSTA RIVER

2021 ◽  
pp. 80-86
Author(s):  
V.A. Naumov ◽  
Keyword(s):  
Linear Regression ◽  
Initial Data ◽  
Water Surface ◽  
Pair Correlation ◽  
Water Levels ◽  
Regression Equations ◽  
Flood Wave ◽  
Short Term ◽  
Daily Water ◽  
Short Term Forecasting

The advanced Mathcad program are proposed for processing large amounts of information about daily water levels in rivers. The results of observations of the hydrological posts of Roshydromet over the level of the Msta River (Neva basin) for 10 years (2009-2018) served as initial data. The analysis showed a close re-lationship to the stochastic slopes of the water surface (SWS) with the water levels. The coefficient of pair correlation between them remained above 0.7 in all years. The range of changes in SWS is rather narrow: 0.166 to 0.194%. The obtained linear regression equations agree quite satisfactorily with the observational data. The determination index is in the range from 0.608 to 0.859. The results of the study can be used for short-term forecasting of the passage of a flood wave.

The causes of poverty in Russia

2020 ◽  
Vol 13 (1) ◽  
pp. 21-36
Author(s):  
I.S. Ivanchenko
Keyword(s):  
Regression Analysis ◽  
Regional Development ◽  
Financial Market ◽  
Russian Federation ◽  
Short Term ◽  
Financial Situation ◽  
Macroeconomic Variables ◽  
Development Plans ◽  
The Russian Federation ◽  
Short Term Forecasting

Subject. This article analyzes the changes in poverty of the population of the Russian Federation. Objectives. The article aims to identify macroeconomic variables that will have the most effective impact on reducing poverty in Russia. Methods. For the study, I used the methods of logical, comparative, and statistical analyses. Results. The article presents a list of macroeconomic variables that, according to Western scholars, can influence the incomes of the poorest stratum of society and the number of unemployed in the country. The regression analysis based on the selected variables reveals those ones that have a statistically significant impact on the financial situation of the Russian poor. Relevance. The results obtained can be used by the financial market mega-regulator to make anti-poverty decisions. In addition, the models built can be useful to the executive authorities at various levels for short-term forecasting of the number of unemployed and their income in drawing up regional development plans for the areas.

Very short-term forecasting of wind power generation using hybrid deep learning model

2021 ◽  
Vol 296 ◽  
pp. 126564
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Deep Learning Model

scholarly journals Improving short-term forecasting during ramp events by means of Regime-Switching Artificial Neural Networks

2011 ◽  
Vol 6 (1) ◽  
pp. 55-58 ◽  
Author(s):  
C. Gallego ◽  
A. Costa ◽  
A. Cuerva
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Regime Switching ◽  
Neural Nets ◽  
Ann Model ◽  
Short Term ◽  
Explanatory Variables ◽  
Ramp Events ◽  
Artificial Neural ◽  
Short Term Forecasting

Abstract. Ramp events are large rapid variations within wind power time series. Ramp forecasting can benefit from specific strategies so as to particularly take into account these shifts in the wind power output dynamic. In the short-term context (characterized by prediction horizons from minutes to a few days), a Regime-Switching (RS) model based on Artificial Neural Nets (ANN) is proposed. The objective is to identify three regimes in the wind power time series: Ramp-up, Ramp-down and No-ramp regime. An on-line regime assessment methodology is also proposed, based on a local gradient criterion. The RS-ANN model is compared to a single-ANN model (without regime discrimination), concluding that the regime-switching strategy leads to significant improvements for one-hour ahead forecasts, mainly due to the improvements obtained during ramp-up events. Including other explanatory variables (NWP outputs, local measurements) during the regime assessment could eventually improve forecasts for further horizons.

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