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1

Application of ARIMA Models for the Analysis of Utilization Process of Military Technical Objects

Borucka A.

Logistics and Transport

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2018

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Vol. 37, No. 1

13--22

EN

The newest solutions in Polish Armed Forces are implemented gradually and focus mainly on soldiers’ combat readiness. Many concurrent processes occur, for which proper analysis and interpretation could constitute command process and task realization support; however poor and standing (paper) record seems to be an obstacle in their modelling. Therefore the author of the article tried to depict the process of military technical objects exploitation based on archived data according to present methods of documents preparation, circuit and record, applicable in Polish Armed Forces. Based on that, the method of research the readiness of aircraft ships from military air base, powered by ARIMA model, was proposed. Using empirical data of two years of exploitation, the identification of researched time series, and then a few models estimation was made. Finally, the best model was chosen and verified.

2

The application of the arima model in forecasting the passenger traffic on the example of border crossings between the subcarpathian province and Ukraine

Dejniak D.

Information Systems in Management

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2018

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Vol. 7, No. 3

155--170

EN

Accession of Poland to the European Union meant that its eastern border became the external frontier of the Community. The next step in the European integration was joining the Schengen Zone by Poland. Polish citizens may freely travel throughout the Schengen Zone and the state was obliged to tighten its eastern border. Under these circumstances conducting research on passenger traffic has become a vital issue, with particular focus on the eastern frontier. In the article an attempt is made at examining the possibility of forecasting passenger traffic on the example of border crossing points between the Subcarpathian Province and Ukraine using the ARIMA models. Confirmation of these possibilities seems to be crucial as the number of people crossing the border is characterized by high variability and sensitivity to the political situation. The study is based on the information provided by the Polish Border Guard. The conducted time series analysis is of a multi-purpose character. It may be used to support decision making processes of investment, organizational, as well as socio-political nature.

3

Matematyczne modele prognozowania popytu na przykładzie Okręgowej Spółdzielni Mleczarskiej

Dąbrowski P., Borucka A., Stępień B.

Gospodarka Materiałowa i Logistyka

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2017

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nr 5

125--149

PL

Prognozowanie popytu jest bardzo istotnym elementem działalności każdej firmy. W artykule dokonano analizy popytu na mleko, generowanego w Okręgowej Spółdzielni Mleczarskiej w Opolu Lubelskim, celem zaproponowania wiarygodnych modeli prognostycznych. Wykorzystując opracowaną przez Boxa i Jenkinsa metodologię, opracowano model ARIMA (Autoregressive Integrated Moving Average).

EN

Demand forecasting is a very important part of any business. In this article were the demand analyzes for milk made, generated in the District Dairy Cooperative in Opole Lubelskie, to propose reliable forecasting models. Using the Box and Jenkins methodology, was the ARIMA (Autoregressive Integrated Moving Average) model worked out.

4

Effect of autocorrelation on the process control charts in monitoring of a coal washing plant

Tasdemir A.

Physicochemical Problems of Mineral Processing

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2012

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Vol. 48, iss. 2

495-512

EN

Traditional statistical process control charts assume that generated process data are normally and independently distributed, i.e. uncorrelated. This research presents the effect of autocorrelation on process control charts to monitor the two quality characteristics of fine coals produced in a coal washing plant for power plant, namely moisture content and ash content. Individual (X) and moving range charts (MR) were constructed to monitor 10 months data. It was determined that even though both data values obey the normal distribution, there is a moderate autocorrelation between their observations. For simulating the autocorrelated data, ARIMA time-series models were used. It was found that X/MR charts showed many false alarms due to the autocorrelation. The ARIMA (1, 0, 1) for moisture content and ARIMA (0, 1, 2) for ash content were determined to be the best models to remove autocorrelation. Compared to large number of false alarms on conventional X/MR charts and on charts applying the Western Electric rules, which assume the data independence, there were much less unusual points on the X/MR charts of residuals (Special Cause Charts). Usage of residual based control charts is suggested when the data are autocorrelated.

5

Reach of river channel filling forecasting by means of autoregression integrated moving a verage method (ARIMA-models)

Baranenko F.F.

Ecological Chemistry and Engineering. A

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2008

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Vol. 15, nr 12

1419-1423

EN

This review considers the application of statistical methods and ARIMA (autoregression integrated moving average) models to rainfall-runoff modeling and flood forecasting have been discussed. This is a relatively emerging field of research, characterized by a wide variety of techniques, an amenity of hulk source data, a possibility of intermodel comparisons, determina-tion its adequacy to observable data and also inconsistent reporting of model skin. The paper outlines the basic principles of ARIMA modeling and algorithms used. Literature survey underlines the need for clear guidance in current ARIMA modeling practice, as well as the comparison of ARIMA models with already existing models of rainfall-runoff. Accordingly, a template is proposed in order to assist the construction of future ARIMA rainfall-runoff models.

PL

Przedstawiono zastosowanie metod statystycznych, w tym zwłaszcza modelu ARIMA (autoregresji całkowanej zmiennej średniej), do prognozowania przebiegu sytuacji powodziowych. Omówiono zastosowanie modelu ARIMA do opisu powsta-wania wód powodziowych spowodowanych ulewnymi deszczami oraz spływu tych wód.

6

Prognozowanie wielkości przychodów ze sprzedaży z wykorzystaniem modeli ARIMA oraz SSN

Chrabołowska J., Halicka K.

Zeszyty Naukowe Politechniki Białostockiej. Ekonomia i Zarządzanie

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2003

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Z. 8

103-116

PL

W artykule przedstawiono metodę budowy modeli ARIMA i SSN oraz wykorzystanie tych modeli do prognozowania jednowymiarowych szeregów czasowych. Opisano i przedyskutowano kolejne etapy tworzenia modelu na przykładzie danych dotyczących przedsiębiorstwa handlowego typu cash & carry, wyznaczono prognozy przychodów ze sprzedaży na kolejne miesiące. Proponowane podejście ilościowe poszerza metodę prognozowania, stosowaną dotychczas w przedsiębiorstwie, wzbogacając jednocześnie informację o decyzji kierownictwa firmy. Narzędziem informatycznym wykorzystanym w procesie opracowania modelu i wyznaczania prognozy był moduł Szeregi czasowe i prognozowanie oraz Statistica Neural Network™ PL programu STATISTICA PL wersji 6.0

EN

The article presents ARIMA and SSN models' methodology of construction and their application for one-dimensional time series forecasting. Consecutive models construction phases have been described and discussed on the example of data concerning a cash & carry type trade enterprise. Sales gross income forecast for consecutive months has been calculated. The quantity approach suggested in the paper broadens methodology of forecasting that has been already implemented in the enterprise and also enriches the decision-making information of company's management.Software used for model creation and forecast calculation were Time Series and Forecasting and Statistica Neural Network™ modules of STATISTICA PL 6.0.

7

Modele ARIMA w prognozowaniu sprzedaży

Chrabołowska J., Nazarko J.

Zagadnienia Techniczno-Ekonomiczne

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2003

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T. 48, z. 3

791--804

PL

W artykule przedstawiono metodykę budowy modeli ARIMA oraz ich wykorzystanie do prognozowania jednowymiarowych szeregów czasowych. Wykorzystano jedno z ogólnie stosowanych podejść zaproponowane przez Boxa i Jenkinsa. Opisano i przedyskutowano kolejne etapy tworzenia modelu na przykładzie danych dotyczących przedsiębiorstwa handlowego typu cash & carry oraz przedsiębiorstwa produkcyjnego.

EN

The paper presents construction methodology of ARIMA models and their application in one-dimensional time series forecasting. The Box and Jenkins approach, being one of the widely used, has been employed. Consecutive phases of the model constructing have been described and discussed on the basis of a cash & carry type of trade as well as productive enterprise.