Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "hybrid forecasting" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-9 z 9
    Tytuł:
    Artificial intelligence-based hybrid forecasting models for manufacturing systems
    Autorzy:
    Rosienkiewicz, Maria
    Powiązania:
    https://bibliotekanauki.pl/articles/1841698.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
    Tematy:
    artificial neural network
    support vector machine
    extreme learning machine
    hybrid forecasting
    production planning
    maintenance
    quality control
    Opis:
    The paper addresses the problem of forecasting in manufacturing systems. The main aim of the research is to propose new hybrid forecasting models combining artificial intelligencebased methods with traditional techniques based on time series – namely: Hybrid econometric model, Hybrid artificial neural network model, Hybrid support vector machine model and Hybrid extreme learning machine model. The study focuses on solving the problem of limited access to independent variables. Empirical verification of the proposed models is built upon real data from the three manufacturing system areas – production planning, maintenance and quality control. Moreover, in the paper, an algorithm for the forecasting accuracy assessment and optimal method selection for industrial companies is introduced. It can serve not only as an efficient and costless tool for advanced manufacturing companies willing to select the right forecasting method for their particular needs but also as an approach supporting the initial steps of transformation towards smart factory and Industry 4.0 implementation.
    Źródło:
    Eksploatacja i Niezawodność; 2021, 23, 2; 263-277
    1507-2711
    Pojawia się w:
    Eksploatacja i Niezawodność
    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:
    Modelling and forecasting monthly Brent crude oil prices: a long memory and volatility approach
    Autorzy:
    AlـGounmeein, Remal Shaher
    Ismail, Mohd Tahir
    Powiązania:
    https://bibliotekanauki.pl/articles/1047374.pdf
    Data publikacji:
    2021-03-03
    Wydawca:
    Główny Urząd Statystyczny
    Tematy:
    ARFIMA
    volatility
    fGARCH
    sGARCH
    modelling and forecasting
    hybrid model
    Opis:
    The Standard Generalised Autoregressive Conditionally Heteroskedastic (sGARCH) model and the Functional Generalised Autoregressive Conditionally Heteroskedastic (fGARCH) model were applied to study the volatility of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model, which is the primary objective of this study. The other goal of this paper is to expand on the researchers' previous work by examining long memory and volatilities simultaneously, by using the ARFIMA-sGARCH hybrid model and comparing it against the ARFIMA-fGARCH hybrid model. Consequently, the hybrid models were configured with the monthly Brent crude oil price series for the period from January 1979 to July 2019. These datasets were considered as the global economy is currently facing significant challenges resulting from noticeable volatilities, especially in terms of the Brent crude prices, due to the outbreak of COVID-19. To achieve these goals, an R/S analysis was performed and the aggregated variance and the Higuchi methods were applied to test for the presence of long memory in the dataset. Furthermore, four breaks have been detected: in 1986, 1999, 2005, and 2013 using the Bayes information criterion. In the further section of the paper, the Hurst Exponent and Geweke-Porter-Hudak (GPH) methods were used to estimate the values of fractional differences. Thus, some ARFIMA models were identified using AIC (Akaike Information Criterion), BIC (Schwartz Bayesian Information Criterion), AICc (corrected AIC), and the RMSE (Root Mean Squared Error). In result, the following conclusions were reached: the ARFIMA(2,0.3589648,2)-sGARCH(1,1) model and the ARFIMA(2,0.3589648,2)-fGARCH(1,1) model under normal distribution proved to be the best models, demonstrating the smallest values for these criteria. The calculations conducted herein show that the two models are of the same accuracy level in terms of the RMSE value, which equals 0.08808882, and it is this result that distinguishes our study. In conclusion, these models can be used to predict oil prices more accurately than others.
    Źródło:
    Statistics in Transition new series; 2021, 22, 1; 29-54
    1234-7655
    Pojawia się w:
    Statistics in Transition new series
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Hybrydy metod badawczych w studiach przyszłości
    Hybrids of research methods in future studies
    Autorzy:
    Magruk, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/399200.pdf
    Data publikacji:
    2012
    Wydawca:
    Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
    Tematy:
    foresight
    koncepcja hybrydowa
    prognozowanie
    synergia
    hybrid concept
    forecasting
    synergy
    Opis:
    In the literature, there are studies on combining research methods. It can be assumed that these combinations often form hybrid structures. This paper describes the various characteristics of hybrid concept found in the sciences of the future. The analysis consists of three fields: first, closely related to foresight methods, the second method is characterized in the context of the forecasting, third involves mixed approaches..
    Źródło:
    Ekonomia i Zarządzanie; 2012, 4, 4; 37-46
    2080-9646
    Pojawia się w:
    Ekonomia i Zarządzanie
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Identification of research areas in fuel sales forecasting within the business ecosystem context: A review, theoretical synthesis, and extension
    Autorzy:
    Zema, Tomasz
    Sulich, Adam
    Hernes, Marcin
    Powiązania:
    https://bibliotekanauki.pl/articles/31234040.pdf
    Data publikacji:
    2024
    Wydawca:
    Uniwersytet Ekonomiczny w Katowicach
    Tematy:
    fuel sales forecasting
    business ecosystems
    hybrid literature review
    petroleum products
    Opis:
    Aim/purpose – This paper aims to explore both fuel sales forecasting and the business ecosystem, subsequently reversing the focus to examine the business ecosystem in the context of fuel sales forecasting. Accompanying this research objective are the following research questions: 1) Does the order in which the topics of “business ecosystems” and “fuel sales forecasting” are searched affect the search results? 2) Which keywords frequently co-occur in publications related to “business ecosystems” and “fuel sales forecasting”? 3) What is the relationship between the terms “fuel sales forecasting” and “business ecosystem”? Design/methodology/approach – The study employs a hybrid review methodology, utilizing specific queries within the Scopus database to identify research themes and motifs. This hybrid form of literature review integrates the tenets of both bibliometric and structured reviews. In this study, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was employed. The visual analysis was conducted using VOSviewer bibliometric software, with a focus on keywords relevant to the relationship between fuel sales forecasting and business ecosystem terms. Findings – Key findings include the identification of co-occurring keywords in fuel sales forecasting and business ecosystem theory literature. The study reveals research gaps and potential areas for future study in business ecosystems, highlighting the impact of fuel sales forecasting in various economic sectors beyond traditional ones, like forestry, agriculture, and fisheries. Utilizing a hybrid literature study research method, the paper analyses data from scientific publications in the Scopus database and employs VOSviewer software to develop bibliometric maps of keyword co-occurrences. Research implications/limitations – The research underscores the broad implications of fuel sales forecasting within a business ecosystem context and identifies areas lacking in-depth study. This study maps scientific publications, identifying the intellectual structure and current research trends. This study contributes to the understanding of fuel sales forecasting within the business ecosystem context as a part of the energy sector transition. Originality/value/contribution – This paper contributes to the field of science and practice by identifying research areas integrating fuel sales forecasting within the business ecosystem construct. It indicates future promising research avenues for researchers and industry professionals, aiming to guide ongoing research. The article addresses a significant theme that warrants scholarly attention. This study allows researchers to define the research gaps covered by published articles and indicate the directions of scientific development.
    Źródło:
    Journal of Economics and Management; 2024, 46; 79-110
    1732-1948
    Pojawia się w:
    Journal of Economics and Management
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Enhancing the performance of deep learning technique by combining with gradient boosting in rainfall-runoff simulation
    Autorzy:
    Abdullaeva, Barno S.
    Powiązania:
    https://bibliotekanauki.pl/articles/28411647.pdf
    Data publikacji:
    2023
    Wydawca:
    Instytut Technologiczno-Przyrodniczy
    Tematy:
    deep learning
    gradient boosting
    hybrid model
    multi-step ahead forecasting
    rainfall-runoff simulation
    Opis:
    Artificial neural networks are widely employed as data mining methods by researchers across various fields, including rainfall-runoff (R-R) statistical modelling. To enhance the performance of these networks, deep learning (DL) neural networks have been developed to improve modelling accuracy. The present study aims to improve the effectiveness of DL networks in enhancing the performance of artificial neural networks via merging with the gradient boosting (GB) technique for daily runoff data forecasting in the river Amu Darya, Uzbekistan. The obtained results showed that the new hybrid proposed model performed exceptionally well, achieving a 16.67% improvement in determination coefficient (R2) and a 23.18% reduction in root mean square error (RMSE) during the training phase compared to the single DL model. Moreover, during the verification phase, the hybrid model displayed remarkable performance, demonstrating a 66.67% increase in R2 and a 50% reduction in RMSE. Furthermore, the hybrid model outperformed the single GB model by a significant margin. During the training phase, the new model showed an 18.18% increase in R2 and a 25% reduction in RMSE. In the verification phase, it improved by an impressive 75% in R2 and a 33.33% reduction in RMSE compared to the single GB model. These findings highlight the potential of the hybrid DL-GB model in improving daily runoff data forecasting in the challenging hydrological context of the Amu Darya River basin in Uzbekistan.
    Źródło:
    Journal of Water and Land Development; 2023, 59; 216--223
    1429-7426
    2083-4535
    Pojawia się w:
    Journal of Water and Land Development
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Missing observations in daily returns - Bayesian inference within the MSF-SBEKK model
    Autorzy:
    Osiewalski, Krzysztof
    Osiewalski, Jacek
    Powiązania:
    https://bibliotekanauki.pl/articles/483257.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Bayesian econometrics
    hybrid MGARCH-MSV processes
    forecasting unavailable data
    financial markets
    commodity markets
    Opis:
    Often daily prices on different markets are not all observable. The question is whether we should exclude from modelling the days with prices not available on all markets (thus loosing some information and implicitly modifying the time axis) or somehow complete the missing (non-existing) prices. In order to compare the effects of each of two ways of dealing with partly available data, one should consider formal procedures of replacing the unavailable prices by their appropriate predictions. We propose a fully Bayesian approach, which amounts to obtaining the marginal posterior (or predictive) distribution for any particular day in question. This procedure takes into account uncertainty on missing prices and can be used to check validity of informal ways of "completing" the data (e.g. linear interpolation). We use the MSF-SBEKK structure, the simplest among hybrid MSV-MGARCH models, which can parsimoniously describe volatility of a large number of prices or indices. In order to conduct Bayesian inference, the conditional posterior distributions for all unknown quantities are derived and the Gibbs sampler (with Metropolis-Hastings steps) is designed. Our approach is applied to daily prices from six different financial and commodity markets; the data cover the period from December 21, 2005 till September 30, 2011, so the time of the global financial crisis is included. We compare inferences (on individual parameters, conditional correlation coefficients and volatilities), obtained in the cases where unavailable observations are either deleted or forecasted.
    Źródło:
    Central European Journal of Economic Modelling and Econometrics; 2012, 4, 3; 169-197
    2080-0886
    2080-119X
    Pojawia się w:
    Central European Journal of Economic Modelling and Econometrics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Performance Comparison of Four New ARIMA-ANN Prediction Models on Internet Traffic Data
    Autorzy:
    Babu, C. N.
    Reddy, B. E.
    Powiązania:
    https://bibliotekanauki.pl/articles/308269.pdf
    Data publikacji:
    2015
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    ANN
    ANN training
    ARIMA
    Box-Jenkins methodology
    hybrid ARIMA-ANN model
    Internet traffic forecasting
    Opis:
    Prediction of Internet traffic time series data (TSD) is a challenging research problem, owing to the complicated nature of TSD. In literature, many hybrids of auto-regressive integrated moving average (ARIMA) and artificial neural networks (ANN) models are devised for the TSD prediction. These hybrid models consider such TSD as a combination of linear and non-linear components, apply combination of ARIMA and ANN in some manner, to obtain the predictions. Out of the many available hybrid ARIMA-ANN models, this paper investigates as to which of them suits better for Internet traffic data. This suitability of hybrid ARIMA-ANN models is studied for both one-step ahead and multistep ahead prediction cases. For the purpose of the study, Internet traffic data is sampled at every 30 and 60 minutes. Model performances are evaluated using the mean absolute error and mean square error measurement. For one-step ahead prediction, with a forecast horizon of 10 points and for three-step prediction, with a forecast horizon of 12 points, the moving average filter based hybrid ARIMA-ANN model gave better forecast accuracy than the other compared models.
    Źródło:
    Journal of Telecommunications and Information Technology; 2015, 1; 67-75
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A review of methods applied for wind power generation forecasting
    Przegląd metod prognozowania produkcji w elektrowniach wiatrowych
    Autorzy:
    Augustyn, A.
    Kamiński, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/283160.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Instytut Gospodarki Surowcami Mineralnymi i Energią PAN
    Tematy:
    wind power
    forecasting
    physical method
    statistical method
    hybrid method
    wind power integration
    energetyka wiatrowa
    prognozowanie
    metoda fizyczna
    metoda statystyczna
    metoda hybrydowa
    integracja energetyki wiatrowej
    Opis:
    The dynamic development of wind power in recent years has generated the demand for production forecasting tools in wind farms. The data obtained from mathematical models is useful both for wind farm owners and distribution and transmission system operators. The predictions of production allow the wind farm operator to control the operation of the turbine in real time or plan future repairs and maintenance work in the long run. In turn, the results of the forecasting model allow the transmission system operator to plan the operation of the power system and to decide whether to reduce the load of conventional power plants or to start the reserve units. The presented article is a review of the currently applied methods of wind power generation forecasting. Due to the nature of the input data, physical and statistical methods are distinguished. The physical approach is based on the use of data related to atmospheric conditions, terrain, and wind farm characteristics. It is usually based on numerical weather prediction models (NWP). In turn, the statistical approach uses historical data sets to determine the dependence of output variables on input parameters. However, the most favorable, from the point of view of the quality of the results, are models that use hybrid approaches. Determining the best model turns out to be a complicated task, because its usefulness depends on many factors. The applied model may be highly accurate under given conditions, but it may be completely unsuitable for another wind farm.
    Dynamiczny rozwój energetyki wiatrowej w ostatnich latach generuje zapotrzebowanie na narzędzia do prognozowania produkcji w elektrowniach wiatrowych. Informacje pozyskane z wykorzystaniem modeli matematycznych są użyteczne zarówno dla właścicieli farm wiatrowych, jak i dla operatorów systemów dystrybucyjnych i przesyłowych. Posiadając informacje dotyczące przewidywanej produkcji, operator elektrowni wiatrowej może sterować pracą turbiny w czasie rzeczywistym lub zaplanować remonty i prace konserwacyjne w przyszłości. Z kolei operator systemu przesyłowego, dysponując wynikami modelu prognostycznego, może zaplanować pracę systemu elektroenergetycznego, decydując się na redukcję obciążenia w elektrowniach konwencjonalnych lub na włączenie jednostek rezerwowych. Niniejszy artykuł przedstawia przegląd obecnie stosowanych metod prognozowania produkcji w elektrowniach wiatrowych. Ze względu na charakter danych wejściowych wyróżnia się metody fizyczne oraz statystyczne. Podejście fizyczne opiera się na wykorzystaniu danych związanych z warunkami atmosferycznymi, ukształtowaniem terenu i charakterystyką farmy wiatrowej. Najczęściej bazuje na modelach numerycznych prognoz pogody NWP (ang. numerical weather prediction). Z kolei w podejściu statystycznym wykorzystuje się zbiory danych historycznych w celu ustalenia zależności zmiennych wyjściowych od parametrów wejściowych. Jednak za najkorzystniejsze pod względem jakości uzyskiwanych wyników uznaje się modele, które wykorzystują podejścia hybrydowe. Określenie najlepszego modelu okazuje się zadaniem skomplikowanym, ponieważ jego użyteczność zależy od wielu czynników. Model zastosowany w danych warunkach może charakteryzować się wysoką dokładnością, natomiast być kompletnie nieprzydatny dla innej farmy wiatrowej.
    Źródło:
    Polityka Energetyczna; 2018, 21, 2; 139-150
    1429-6675
    Pojawia się w:
    Polityka Energetyczna
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-9 z 9

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies