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Tytuł:
Varimax Model to Forecast the Emission of Carbon Dioxide from Energy Consumption in Rubber and Petroleum Industries Sectors in Thailand
Autorzy:
Sutthichaimethee, P.
Powiązania:
https://bibliotekanauki.pl/articles/125458.pdf
Data publikacji:
2017
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
Rubber, Chemical and Petroleum Industries sectors
population
forecasting model
energy consumption
CO2 emission
GDP growth
Opis:
This study aims to analyze the forecasting of CO2 emission from the energy consumption in the Rubber, Chemical and Petroleum Industries sectors in Thailand. The scope of research employed the input-output table of Thailand from the year 2000 to 2015. It was used to create the model of CO2 emission, population, GDP growth and predict ten years and thirty years in advance. The model used was the VARIMAX Model which was divided into two models. The results show that from the first model by using which predicted the duration of ten years (2016–2025) by using VARIMAX Model (2,1,2), On average, Thailand has 17.65% higher quantity of CO2 emission than the energy consumption sector (in 2025). The second model predicted the duration of 30 years (2016–2045) by using VARIMAX Model (2,1,3) shows that Thailand has average 39.68% higher quantity of CO2 emission than the energy consumption sector (in 2025). From the analyses, it shows that Thailand has continuously higher quantity of CO2 emission from the energy consumption. This negatively affects the environmental system and economical system of the country incessantly. This effect can lead to unsustainable development.
Źródło:
Journal of Ecological Engineering; 2017, 18, 3; 112-117
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of the Statistical Error and Quantitative Performance Measures in the Evaluation Process of Short-Term Air Quality Forecasts for Krakow (Poland)
Zastosowanie statystycznych miar błędów i wskaźników wydajności modelu w procesie oceny krótkoterminowych prognoz jakości powietrza w Krakowie (Polska)
Autorzy:
Szulecka, A.
Mazur, M.
Powiązania:
https://bibliotekanauki.pl/articles/385805.pdf
Data publikacji:
2016
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
jakość powietrza
stężenia zanieczyszczeń
modelowanie prognostyczne
GEM-AQ
ocena statystyczna
air quality
pollutant concentrations
forecasting model
statistical evaluation
Opis:
Obecnie matematyczne modelowanie prognozowania jakości powietrza ma duże znaczenie ze względu na potrzebę informowania ludności o spodziewanych stężeniach zanieczyszczeń i wydawania odpowiednich ostrzeżeń. Na każdym etapie stosowania systemu modelowania wymagana jest dokładna weryfi kacja i diagnostyka jego wydajności. W pracy przedstawiono wyniki oceny statystycznej systemu krótkoterminowych prognoz jakości powietrza na obszarze Krakowa (Polska) w okresie kwiecień 2014 – marzec 2015. Prognozy opierają się na systemie modelowania opracowanym przez fundację EkoPrognoza i udostępnianym Politechnice Warszawskiej. Obliczenia wykonywano za pomocą modelu GEM-AQ, a ich wynikiem są publicznie dostępne prognozy średnich dziennych stężeń PM10, PM2,5, NO2 , SO2 , CO oraz O3 . W trakcie prowadzonych badań wartości te zostały porównane z wynikami obserwacji pomiarowych rejestrowanych na stacji tła miejskiego w Krakowie (ul. Bujaka) przy użyciu statystyk błędów i mierników wydajności modelu zalecanych przez Amerykańską Agencję Ochrony Środowiska. Wyniki przeprowadzonych obliczeń wskazują na dobrą sprawdzalność prognoz stężeń PM10 oraz PM2,5 w okresie analizy, co skutkuje ich silną korelacją z wynikami pomiarów. Oceniany model przejawia tendencję do przeszacowywania wszystkich prognoz w odniesieniu do pomiarów stężeń substancji gazowych na stacji przy ul. Bujaka. Największe rozbieżności dotyczą prognoz stężenia dwutlenku siarki (SO2) oraz ozonu (O3) i są charakterystyczne głównie dla sezonu pozagrzewczego. Niedokładność prognoz wpływa na wiarygodność przewidywanej wartości wspólnego indeksu jakości powietrza (CAQI), będącego wypadkową stężeń poszczególnych zanieczyszczeń powietrza.
Currently, mathematical modelling air quality forecasts is of great importance due to the need of informing the population about the upcoming concentrations of air pollutants and issuing accurate alerts. At each stage in the application of a modelling system a proper verification and performance diagnostics is required. This paper presents the results of a statistical evaluation of the short-term air quality forecasting system for the area of Krakow, Poland, over the period of April 2014 – March 2015. The analysed forecasts are prepared by Warsaw University of Technology on the basis of the modelling system created by the EkoForecast foundation. Calculations in this system are performed by the GEM-AQ model, which produces publicly available predictions of the daily average concentrations of PM10, PM2.5, NO2 , SO2 , CO and O3 . In this study these values were compared to the measured observations recorded at the urban background station in Krakow (Bujaka St.) with the use of error statistics and quantitative performance measures suggested by the US EPA. The results of the analysis indicate good reliability of PM10 and PM2.5 forecasted concentrations during the examined period of time, which provides high correlation rates for these observations. Evaluated model tends to overestimate all the predictions in reference to Bujaka St. station measurements. The highest discrepancies are evident in the case of sulphur dioxide (SO2) and ozone (O3) predictions occurring mainly during the non-heating season. Insuffi cient forecast accuracy aff ects the reliability of the predicted Common Air Quality Index (CAQI), which depends on the concentration of particular air pollutants.
Źródło:
Geomatics and Environmental Engineering; 2016, 10, 3; 87-99
1898-1135
Pojawia się w:
Geomatics and Environmental Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A simplified forecasting model for the estimation of container traffic in seaports at a national level – the case of Poland
Autorzy:
Matczak, M.
Powiązania:
https://bibliotekanauki.pl/articles/116932.pdf
Data publikacji:
2020
Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Tematy:
containerization
Polish seaport sector
simplified forecasting model
container traffic in seaports
estimation of container traffic in seaports
TEU
container
Opis:
Comprehensive forecasting of future volumes of container traffic in seaports is important when it comes to port development, including investments, especially in relation to costly transport infrastructure (e.g. new terminals). The aim of this article is to present a specific, simplified model of demand forecasting for container traffic in seaports as well as to give a practical verification of the model in the Polish seaport sector. The model consists of relevant indexes of containerisation (values, dynamics) referring to the macroeconomic characteristics of the country of cargo origin as well as destination-predictor variables (e.g. population, foreign trade, gross domestic product). This method will facilitate the evaluation of three basic segments of the container market: foreign trade services, maritime transit flows and land transit flows. International comparisons of indexes (benchmarking) as well as extrapolations of future changes can support this prediction process. A practical implementation of this research has enabled us to calculate that the total container volume in Poland will be approximately 4.69 – 4.87 million TEU by the year 2023.
Źródło:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2020, 14, 1; 153-158
2083-6473
2083-6481
Pojawia się w:
TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Residential electricity consumption in Poland
Autorzy:
Ropuszyńska-Surma, E.
Węglarz, M.
Powiązania:
https://bibliotekanauki.pl/articles/406401.pdf
Data publikacji:
2016
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
forecasting
demand forecasting
econometric model
electricity consumption
HDD index
Opis:
Key factors influencing electricity consumption in the residential sector in Poland have been identified. A fixed-effects model was used, which includes time effects, and a set of covariates, based on the model developed by Houthakker et al. This model estimates electricity demand by using lagged values of the dependent variable along with current and lagged values of electricity prices, and other variables that affect electricity demand such as: population, economic growth, income per capita, price of related goods, etc. The model has been identified according to the research results of the authors and those obtained by Bentzen and Engsted. The set of covariates was extended to the lagged electricity price given by a tariff (taken from two years previous to the time of interest) and heating degree days index, a very important factor in European Union countries, where the climate is temperate. The authors propose four models of residential electricity demand, for which a confidence interval of 95% has been assumed. Estimation was based on Polish quarterly data for the years 2003–2013.
Źródło:
Operations Research and Decisions; 2016, 26, 3; 69-82
2081-8858
2391-6060
Pojawia się w:
Operations Research and Decisions
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application out-of-sample forecasting in model selection on Nigeria exchange rate
Autorzy:
Henry, Akpensuen Shiaondo
Lasisi, K. E.
Akpan, E. A.
Gwani, A. A.
Powiązania:
https://bibliotekanauki.pl/articles/1062858.pdf
Data publikacji:
2019
Wydawca:
Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
Tematy:
ARMA model
Exchange Rate
In-sample forecasting
Model selection and evaluation
Out-sample forecasting
Opis:
In time series, several competing models may adequately fit a given set of data. At times choosing the best model may be easy or difficult. However, there are two major model selection criteria; it could be either in-sample or out-of-sample forecasts. This study was necessitated because Empirical evidence based on out-of-sample model forecast performance is generally considered more trustworthy than evidence based on in-sample model performance which can be more sensitive to outliers and data mining. And also the fact that Out-of-sample forecasts also better reflect the information available to the forecaster in real time was also an added motivation. Hence this study considered data from Nigeria exchange rate (Naira to US Dollar) from January 2002 to December 2018 comprising 204 observations. The first 192 observations were used for model identification and estimation while the remaining 12 observations were holdout for forecast validation. Three ARIMA models; ARIMA (0, 1, 1), ARIMA (1, 1, 2) and ARIMA (2, 1, 0) were fitted tentatively. Base on in-sample information criteria ARIMA (0, 1, 1) was the best model with minimum AIC, SIC and HQ information criteria. However, on the basics of out-of-sample forecast evaluation using RMSE, MSE, MAE, and MAPE, ARIMA (2, 1, 0) perform better than ARIMA (0, 1, 1). The implication of this study is that, a model that is best in the in-sample fitting may not necessary give a genuine forecasts since it is the same data that is used in model identification and estimation that is also use in forecast evaluation.
Źródło:
World Scientific News; 2019, 127, 3; 225-247
2392-2192
Pojawia się w:
World Scientific News
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
eBalticGrid – an interactive platform for the visualisation of results from a high-resolution operational Baltic Sea model
Autorzy:
Jakacki, J.
Przyborska, A.
Nowicki, A.
Wichorowski, M.
Przyborski, M.
Białoskórski, M.
Sochacki, C.
Tylman, R.
Powiązania:
https://bibliotekanauki.pl/articles/108447.pdf
Data publikacji:
2017
Wydawca:
Instytut Meteorologii i Gospodarki Wodnej - Państwowy Instytut Badawczy
Tematy:
multimodelling
Baltic Sea
forecasting
hydrodynamic model
ice model
Opis:
In recent years, modelling has been one of the fastest growing fields of science. Ocean, ice and atmospheric models have become a powerful tool that has supported many scientific fields during the last few decades. Our work presents the new operational service – called eBalticGrid – implemented into the PLGrid Infrastructure (Dziekoński et al. 2014). The grid is based on three modelling tools – an ocean model (Parallel Ocean Program), an ice model (Community Ice Code) and an atmospheric model (Whether Research and Forecasting Model). The service provides access to 72-hour forecasts for the Baltic Sea area. It includes the physical state of the Baltic Sea, its ice cover and the main atmospheric fields, which are the key drivers of the Baltic’s physical state. Unlike other services, this provides the additional three-dimensional fields of temperature, salinity and currents in the Baltic Sea. The models work in operational mode and currently one simulation per day is run. The service has been implemented mostly for researchers. Access to the results does not require any modelling knowledge. Therefore, the main interface between a user and the model results was designed as a portal providing easy access to the model’s output. It will also be a very suitable tool for teaching students about the hydrology of the Baltic Sea. Data from the system are delivered to another operational system – SatBaltic (Woźniak et al. 2011). The development of an output format to be suitable for navigational software (GRIB files) and sharing via FTP is also planned.
Źródło:
Meteorology Hydrology and Water Management. Research and Operational Applications; 2017, 5, 2; 13-20
2299-3835
2353-5652
Pojawia się w:
Meteorology Hydrology and Water Management. Research and Operational Applications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Note on Option Pricing with the Use of Discrete-Time Stochastic Volatility Processes
Autorzy:
Pajor, Anna
Powiązania:
https://bibliotekanauki.pl/articles/483255.pdf
Data publikacji:
2009
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
option pricing
SV model
Bayesian forecasting
Opis:
In this paper we show that in the lognormal discrete-time stochastic volatility model with predictable conditional expected returns, the conditional expected value of the discounted payoff of a European call option is infinite. Our empirical illustration shows that the characteristics of the predictive distributions of the discounted payoffs, obtained using Monte Carlo methods, do not indicate directly that the expected discounted payoffs are infinite.
Źródło:
Central European Journal of Economic Modelling and Econometrics; 2009, 1, 1; 71-81
2080-0886
2080-119X
Pojawia się w:
Central European Journal of Economic Modelling and Econometrics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Forecasting production volume in a plastics enterprise
Autorzy:
Grzelak, Małgorzata
Powiązania:
https://bibliotekanauki.pl/articles/970665.pdf
Data publikacji:
2019-03-31
Wydawca:
Uniwersytet Gdański. Wydział Ekonomiczny
Tematy:
multiple regression model
production scheduling
readiness
forecasting
Opis:
The functioning of production enterprises is based on satisfying the needs of customers through the timely manufacture of products in accordance with the demand existing on the market. The availability of the offered range of products is guaranteed by a correct preparation of forecasts of potential orders. This article presents a multiple-regression-method-based tool supporting the planning of production volumes in an enterprise depending on the calendar month. Reliability analysis of the developed model through the analysis of residuals and their autocorrelations and partial autocorrelations is also presented. Key words: multiple regression model, production scheduling, readiness, forecasting JEL classification: C2, C22.
Źródło:
Współczesna Gospodarka; 2019, 10, 1 (32); 69-78
2082-677X
Pojawia się w:
Współczesna Gospodarka
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Forecasting the number of road accidents in Poland using weather-dependent trend models
Autorzy:
Gorzelańczyk, Piotr
Powiązania:
https://bibliotekanauki.pl/articles/2203615.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Warmińsko-Mazurski w Olsztynie
Tematy:
traffic accident
forecasting
trend model
weather conditions
Opis:
Every year a very large number of people die on the roads. From year to year, the value decreases, there are still a very high number of them. The pandemic has reduced the number of road accidents, but the value is still very high. For this reason, it is necessary to know under which weather conditions the highest number of road accidents occur, and to know the forecast of accidents according to the prevailing weather conditions for the coming years, in order to be able to do everything possible to minimize the number of road accidents. The purpose of the article is to make a forecast of the number of road accidents in Poland depending on the prevailing weather conditions. The research was divided into two parts. The first was the analysis of annual data from the Police statistics on the number of road accidents in Poland in 2001-2021, and on this basis the forecast of the number of road accidents for 2022-2031 was determined. The second part of the research, dealt with monthly data from 2007-2021. Again, the analyzed forecast for the period January 2022-December 2023 was determined. The results of the study indicate that we can still expect a decline in the number of accidents in the coming years, which is particularly evident when analyzing annual data. It is worth noting that the prevailing pandemic distorts the results obtained. The research was conducted in MS Excel, using selected trend models.
Źródło:
Technical Sciences / University of Warmia and Mazury in Olsztyn; 2023, 26(1); 57--76
1505-4675
2083-4527
Pojawia się w:
Technical Sciences / University of Warmia and Mazury in Olsztyn
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Hybrid demand forecasting models: pre-pandemic and pandemic use studies
Autorzy:
Kolkova, Andrea
Rozehnal, Petr
Powiązania:
https://bibliotekanauki.pl/articles/22443157.pdf
Data publikacji:
2022
Wydawca:
Instytut Badań Gospodarczych
Tematy:
forecastHybrid
demand forecasting
statistic model
neural networks
Opis:
Research background: In business practice and academic sphere, the question of which of the prognostic models is the most accurate is constantly present. The accuracy of models based on artificial intelligence and statistical models has long been discussed. By combining the advantages of both groups, hybrid models have emerged. These models show high accuracy. Moreover, the question remains whether data in a dynamically changing economy (for example, in a pandemic period) have changed the possibilities of using these models. The changing economy will continue to be an important element in demand forecasting in the years to come. In business, where the concept of just in time already proves to be insufficient, it is necessary to open new research questions in the field of demand forecasting. Purpose of the article: The aim of the article is to apply hybrid models to bicycle sales e-shop data with a comparison of accuracy models in the pre-pandemic period and in the pandemic period. The paper examines the hypothesis that the pandemic period has changed the accuracy of hybrid models in comparison with statistical models and models based on artificial neural networks. Models: In this study, hybrid models will be used, namely the Theta model and the new forecastHybrid, compared to the statistical models ETS, ARIMA, and models based on artificial neural networks. They will be applied to the data of the e-shop with the cycle assortment in the period from 1.1. 2019 to 5.10 2021. Whereas the period will be divided into two parts, pre-pandemic, i.e. until 1 March 2020 and pandemic after that date. The accuracy evaluation will be based on the RMSE, MAE, and ACF1 indicators. Findings & value added: In this study, we have concluded that the prediction of the Hybrid model was the most accurate in both periods. The study can thus provide a scientific basis for any other dynamic changes that may occur in demand forecasting in the future. In other periods when there will be volatile demand, it is essential to choose models in which accuracy will decrease the least. Therefore, this study provides guidance for the use of methods in future periods as well. The stated results are likely to be valid even in an international comparison.
Źródło:
Equilibrium. Quarterly Journal of Economics and Economic Policy; 2022, 17, 3; 699-725
1689-765X
2353-3293
Pojawia się w:
Equilibrium. Quarterly Journal of Economics and Economic Policy
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The Warsaw Stock Exchange Index WIG: Modeling and Forecasting
Modelowanie i prognozowanie indeksu WIG
Autorzy:
Wdowiński, Piotr
Zglińska-Pietrzak, Aneta
Powiązania:
https://bibliotekanauki.pl/articles/907597.pdf
Data publikacji:
2005
Wydawca:
Uniwersytet Łódzki. Wydawnictwo Uniwersytetu Łódzkiego
Tematy:
Warsaw Stock Exchange
stock index
GARCH model
forecasting
Opis:
In this paper we have assessed an influence of the NYSE Stock Exchange indexes (DJIA and NASDAQ) and European Stock indexes (DAX and FTSE) on the Warsaw Stock Exchange index WIG within a framework of a GARCH model. By applying a procedure of checking predictive quality of econometric models as proposed by Fair and Shiller (1990), we have found that the NYSE market has relatively more power than European market in explaining the WSE index WIG.
W artykule podjęliśmy próbę oceny wpływu indeksów rynku amerykańskiego DJIA i NASDAQ oraz indeksów rynku europejskiego DAX i FTSE na indeks WIG z giełdy w Warszawie. Do modelowania tego wpływu wykorzystaliśmy metodologię GARCH. Stosując metodologię łączenia prognoz oraz metodologię oceny jakości prognostycznej modeli ekonometrycznych, zaproponowane w pracy Fair i Shiller (1990), pokazaliśmy, że rynek NYSE ma względną przewagę nad rynkiem europejskim w wyjaśnieniu zmian indeksu WIG.
Źródło:
Acta Universitatis Lodziensis. Folia Oeconomica; 2005, 192
0208-6018
2353-7663
Pojawia się w:
Acta Universitatis Lodziensis. Folia Oeconomica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Use of MLCM3 Software for Flash Flood Modeling and Forecasting
Autorzy:
Sokolova, D.
Kuzmin, V.
Batyrov, A.
Pivovarova, I.
Tran, N. A.
Dang, D.
Shemanaev, K. V.
Powiązania:
https://bibliotekanauki.pl/articles/124618.pdf
Data publikacji:
2018
Wydawca:
Polskie Towarzystwo Inżynierii Ekologicznej
Tematy:
hydrological model
flood forecasting
river basins
changing climate
Opis:
Accurate and timely flash floods forecasting, especially, in ungauged and poorly gauged basins, is one of the most important and challenging problems to be solved by the international hydrological community. In changing climate and variable anthropogenic impact on river basins, as well as due to low density of surface hydrometeorological network, flash flood forecasting based on “traditional” physically based, or conceptual, or statistical hydrological models often becomes inefficient. Unfortunately, most of river basins in Russia are poorly gauged or ungauged; besides, lack of hydrogeological data is quite typical. However, the developing economy and population safety necessitate issuing warnings based on reliable forecasts. For this purpose, a new hydrological model, MLCM3 (Multi-Layer Conceptual Model, 3rd generation) has been developed in the Russian State Hydrometeorological University. The model showed good results in more than 50 tested basins.
Źródło:
Journal of Ecological Engineering; 2018, 19, 1; 177-185
2299-8993
Pojawia się w:
Journal of Ecological Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Demand forecasting: an alternative approach based on technical indicator Pbands
Autorzy:
Kolková, Andrea
Ključnikov, Aleksandr
Powiązania:
https://bibliotekanauki.pl/articles/19233720.pdf
Data publikacji:
2021
Wydawca:
Instytut Badań Gospodarczych
Tematy:
demand forecasting
neural network
BATS
hybrid model
Pbands
Opis:
Research background: Demand forecasting helps companies to anticipate purchases and plan the delivery or production. In order to face this complex problem, many statistical methods, artificial intelligence-based methods, and hybrid methods are currently being developed. However, all these methods have similar problematic issues, including the complexity, long computing time, and the need for high computing performance of the IT infrastructure. Purpose of the article: This study aims to verify and evaluate the possibility of using Google Trends data for poetry book demand forecasting and compare the results of the application of the statistical methods, neural networks, and a hybrid model versus the alternative possibility of using technical analysis methods to achieve immediate and accessible forecasting. Specifically, it aims to verify the possibility of immediate demand forecasting based on an alternative approach using Pbands technical indicator for poetry books in the European Quartet countries. Methods: The study performs the demand forecasting based on the technical analysis of the Google Trends data search in case of the keyword poetry in the European Quartet countries by several statistical methods, including the commonly used ETS statistical methods, ARIMA method, ARFIMA method, BATS method based on the combination of the Cox-Box transformation model and ARMA, artificial neural networks, the Theta model, a hybrid model, and an alternative approach of forecasting using Pbands indicator.  The study uses MAPE and RMSE approaches to measure the accuracy. Findings & value added: Although most currently available demand prediction models are either slow or complex, the entrepreneurial practice requires fast, simple, and accurate ones. The study results show that the alternative Pbands approach is easily applicable and can predict short-term demand changes. Due to its simplicity, the Pbands method is suitable and convenient to monitor short-term data describing the demand. Demand prediction methods based on technical indicators represent a new approach for demand forecasting. The application of these technical indicators could be a further forecasting models research direction. The future of theoretical research in forecasting should be devoted mainly to simplifying and speeding up. Creating an automated model based on primary data parameters and easily interpretable results is a challenge for further research.
Źródło:
Oeconomia Copernicana; 2021, 12, 4; 1063-1094
2083-1277
Pojawia się w:
Oeconomia Copernicana
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Time series analysis of reference crop evapotranspiration for Bokaro District, Jharkhand, India
Analiza serii czasowych ewapotranspiracji potencjalnej upraw w dystrykcie Bokaro, Jharkhand, Indie
Autorzy:
Gautam, R.
Sinha, A. K.
Powiązania:
https://bibliotekanauki.pl/articles/293179.pdf
Data publikacji:
2016
Wydawca:
Instytut Technologiczno-Przyrodniczy
Tematy:
ARIMA model
evapotranspiration
forecasting
time series
ewapotranspiracja
model ARIMA
prognozowanie
serie czasowe
Opis:
Evapotranspiration is the one of the major role playing element in water cycle. More accurate measurement and forecasting of Evapotranspiration would enable more efficient water resources management. This study, is therefore, particularly focused on evapotranspiration modelling and forecasting, since forecasting would provide better information for optimal water resources management. There are numerous techniques of evapotranspiration forecasting that include autoregressive (AR) and moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), Thomas Feiring, etc. Out of these models ARIMA model has been found to be more suitable for analysis and forecasting of hydrological events. Therefore, in this study ARIMA models have been used for forecasting of mean monthly reference crop evapotranspiration by stochastic analysis. The data series of 102 years i.e. 1224 months of Bokaro District were used for analysis and forecasting. Different order of ARIMA model was selected on the basis of autocorrelation function (ACF) and partial autocorrelation (PACF) of data series. Maximum likelihood method was used for determining the parameters of the models. To see the statistical parameter of model, best fitted model is ARIMA (0, 1, 4) (0, 1, 1)12.
Ewapotranspiracja jest jednym z głównych elementów obiegu wody. Dokładniejsze pomiary i możliwość prognozowania ewapotranspiracji mogłyby umożliwić wydajniejsze zarządzanie zasobami wodnymi. Dlatego prezentowane w niniejszej pracy badania skoncentrowane były na modelowaniu i prognozowaniu ewapotranspiracji, ponieważ prognozowanie zapewni więcej informacji do optymalnego zarządzania zasobami wodnymi. Istnieje wiele technik prognozowania ewapotranspiracji, takich jak autoregresja (AR), średnia ruchoma (MA), autoregresyjna średnia ruchoma (ARMA), autoregresyjna zintegrowana średnia ruchoma (ARIMA), metoda Thomasa– Feiringa i inne. Stwierdzono, że spośród nich ARIMA jest bardziej odpowiednia do analizy i prognozowania zdarzeń hydrologicznych. Z tego powodu wykorzystano model ARIMA do prognozowania miesięcznych średnich wartości ewapotranspiracji potencjalnej poprzez analizę stochastyczną. Do analiz i prognozowania użyto serii danych ze 102 lat (1224 miesiące) z dystryktu Bokaro. Na podstawie funkcji autokorelacji (ACF) i cząstkowych autokorelacji (PACF) serii danych wybrano różny porządek modelu ARIMA. Do wyznaczenia parametrów modelu wykorzystano metodę maksymalnego prawdopodobieństwa. Najlepiej dostosowanymi parametrami statystycznymi modelu okazały się ARIMA (0, 1, 4) (0, 1, 1)12.
Źródło:
Journal of Water and Land Development; 2016, 30; 51-56
1429-7426
2083-4535
Pojawia się w:
Journal of Water and Land Development
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Impact of econometric modeling and perspectives of economic security of the cross-industry complex
Autorzy:
Liezina, Anastasiia
Lavruk, Alexandr
Matviienko, Halyna
Ivanets, Iryna
Tseluiko, Oleksii
Kuchai, Oksana
Powiązania:
https://bibliotekanauki.pl/articles/2201980.pdf
Data publikacji:
2023
Wydawca:
Centrum Badań i Innowacji Pro-Akademia
Tematy:
economic security
intersectoral complex
econometric model
forecasting
bezpieczeństwo ekonomiczne
model ekonometryczny
prognozowanie
Opis:
The paper presents a forecast of the economic security of the inter-industry complex through the construction of a simulation model. The authors considered the possibility of using an econometric model in predicting the level of economic security of the inter-industry complex. The goal was to form a definition of the "inter-industry complex", as well as to study the issues of conceptual and fundamental methods of econometric modeling and forecasting the development of regional industry markets in dynamics. A range of issues related to the main components of economic security in the inter-industry complex has been allocated for scientific work in order to analyze the impact of the components of economic security on the integral indicator. The paper uses a methodology for predicting the structural and spatial-temporal dynamics of interbranch complexes, which includes new and refined methods of modeling and forecasting. As a result, the authors proposed the definition of "inter-industry complex", "economic security in the inter-industry complex", as well as the general provisions of the methodology for econometric modeling and forecasting the level of economic security of the inter-industry complex. The paper presents a full-scale simulation model that allows you to set, evaluate and make a decision using large nonlinear data. This kind of system contains dynamic and retarded data, which makes it possible to apply econometric modeling in automatic calculation.
Źródło:
Acta Innovations; 2023, 47; 73--83
2300-5599
Pojawia się w:
Acta Innovations
Dostawca treści:
Biblioteka Nauki
Artykuł
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