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Abstract

The size and distribution of water demand within a given structural unit is the basis for the proper operation and planning of the expansion and modernization of the water supply system’s elements. In rural areas, particularly in municipalities adjacent to urban-industrial agglomerations, a change in the use of tap water has been increasingly observed. The water consumption for animal breeding or agricultural use, typical of these areas, has been decreasing and even disappearing. Water has been increasingly used for domestic purposes in single- and multi-family housing as well as for other purposes such as watering lawns and filling residential swimming pools. Taking this into account, this paper presents observations regarding daily water consumption in a municipality adjacent to Wrocław together with an analysis of the possibility of using the exponential smoothing method for the short-term forecasting of daily water consumption. The analyses presented in this paper were carried out using STATISTICA 13 software.
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Authors and Affiliations

Wojciech Cieżak
1
ORCID: ORCID
Małgorzata Kutyłowska
1
ORCID: ORCID

  1. Wrocław University of Science and Technology, Faculty of Environmental Engineering, Wybrzeze Wyspianskiego 27, 50-370 Wrocław, Poland
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Abstract

Coronavirus infection (COVID-19) is a highly infectious disease of viral etiology. SARS-CoV-2 virus was first identified during the investigation of the outbreak of respiratory disease in Wuhan, China in December 2019. And already on March 11, 2020 COVID-19 in the world was characterized by the WHO as a pandemic. In Ukraine the situation with incidence COVID-19 remains difficult. The purpose of this study is to to develop a mathematical forecasting model for COVID-19 incidence in Ukraine using an exponential smoothing method. The article analyzes reports on basic COVID-19 incidence rates from 29.02.2019 to 01.10.2021. In order to determine the forecast levels of statistical indicators that characterize the epidemic process of COVID-19 the method of exponential smoothing was used. It is expected that from 29.02.2019 to 01.10.2021 the epidemic situation of COVID-19 incidence will stabilize. The indicator of “active patients” will range from 159.04 to 353.63 per 100 thousand people. The indicator of “hospitalized patients” can reach 15.43 and “fatalities” — 1.87. The use of the method of exponential smoothing based on time series models for modeling the dynamics of COVID-19 incidence allows to develop and implement scientifically sound methods in order to prevent, quickly prepare health care institutions for hospitalization.
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Authors and Affiliations

Nina Malysh
1
Alla Podavalenko
2
Olga Kuzmenko
3
Svitlana Kolomiets
3

  1. Department of Infectious Diseases with Epidemiology, Sumy State University, Rymskogo-Korsakova 2, Sumy, Ukraine
  2. Department of Hygiene, Epidemiology and Occupational Diseases, Kharkiv Medical Academy of Postgraduate Education, Amosova, 58, Kharkiv, Ukraine
  3. Department of Economic Cybernetics, Sumy State University, Rymskogo-Korsakova 2, Sumy, Ukraine
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Abstract

The aim of this paper is to examine the problem of existing seasonal volatility in total and disaggregated HICP for Baltic Region countries (Denmark, Estonia, Latvia, Finland, Germany, Lithuania, Poland and Sweden). Using nonparametric tests, we found that in the case of m-o-m prices, including fruit, vegetables, and total HICP, the homogeneity of variance during seasons is rejected. Based on these findings, we propose an exponential smoothing model with periodic variance of error terms that capture the repetitive seasonal variation (in conditional or unconditional second moments). In a pseudo-real data experiment, the short-term forecasts (nowcasting) for the considered components of inflation were determined using different specifications of considered models. The forecasting performance of the models was measured using one of the scoring rules for probabilistic forecasts called logarithmic score. We found instead that while the periodic phenomenon in variance was statistically significant, the models with a periodic phenomenon in variance of error terms do not significantly improve forecasting performance in disaggregated cases and in the case of total HICP. The simpler models with constant variance of error term have comparative forecasting (nowcasting) performance over the alternative model.

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Authors and Affiliations

Łukasz Lenart
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Abstract

Time series analysis ofmonthly and daily SO2 data were considered for the detection of trends in SO2 due to possible effect of the emission abatement strategy in the Black Triangle region. Using a time series model, the main components were extracted from the original SO2 time series. Based on SO2 monitoring data from Czerniawa in Izery Mountains in Poland over the period 1993 -1998, our findings showed evidence of declining trends in SO2• A mean annual change of 14.1% was recorded in a 6-year record. It has also appeared that the exponential smoothing which considers a seasonal component and trends provided a reasonable fit to monthly mean SO2 values.
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Authors and Affiliations

Jerzy Zwoździak
Artur Gzella
Anna Zwoździak
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Abstract

Indonesia is widely known as a country with rich biodiversity. Medicinal plants that thrive in Indonesia are utilized as traditional medicine locally known as “jamu”. One of the islands famous for jamu production is Madura Island. As a well-known jamu producer, Madura Island are facing problems related to jamu production. Procurement of medicinal plants is not well controlled. There are no reports of spices procurement and production. When there is an increase in demand or sale of certain jamu, the stock of jamu is commonly inadequate/insufficient This may result in order cancellation. The solution to this problem is to create a production forecasting information system by using single exponential smoothing. The data used is a weekly report on the number of sales of 3 types of jamu from August to October 2024. Mean Absolute Percentage Error (MAPE) testing using an alpha value of 0.1 to 0.9 resulted in “high” accuracy and the forecasted values were close to the actual data values.
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Authors and Affiliations

Rika YUNITARINI
Muhamad Afif EFFINDI
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Abstract

Most of the existing statistical forecasting methods utilize the historical values of wind power to provide wind power generation prediction. However, several factors including wind speed, nacelle position, pitch angle, and ambient temperature can also be used to predict wind power generation. In this study, a wind farm including 6 turbines (capacity of 3.5 MW per turbine) with a height of 114 meters, 132-meter rotor diameter is considered. The time-series data is collected at 10-minute intervals from the SCADA system. One period from January 04th, 2021 to January 08th, 2021 measured from the wind turbine generator 06 is investigated. One period from January 01st, 2021 to January 31st, 2021 collected from the wind turbine generator 02 is investigated. Therefore, the primary objective of this paper is to propose a combined method for wind power generation forecasting. Firstly, response surface methodology is proposed as an alternative wind power forecasting method. This methodology can provide wind power prediction by considering the relationship between wind power and input factors. Secondly, the conventional statistical forecasting methods consisting of autoregressive integrated moving average and exponential smoothing methods are used to predict wind power time series. Thirdly, response surface methodology is combined with autoregressive integrated moving average or exponential smoothing methods in wind power forecasting. Finally, the two above periods are performed in order to demonstrate the efficiency of the combined methods in terms of mean absolute percent error and directional statistics in this study.
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Authors and Affiliations

Tuan-Ho Le
1
ORCID: ORCID

  1. Faculty of Engineering and Technology, Quy Nhon University, Quy Nhon, Binh Dinh Province, 820000, Vietnam

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