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Tytuł:
The impact of the size of the training set on the predictive abilities of neural models on the example of the Day-Ahead Market System of TGE S.A.
Autorzy:
Ruciński, Dariusz
Powiązania:
https://bibliotekanauki.pl/articles/2175162.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
Day Ahead Market
MATLAB environment
Simulink environment
neural modeling
prediction time
electricity prices
Opis:
The main object of the research was to examine the acceptable time horizon that could be predicted by previously learned models of the Day-Ahead Market (DAM) TGE S.A. system. The article contains the results of research on the predicting ability of different ANN models of the DAM TGE S.A. The research was conducted based on data covering the operation of the Polish stock exchange in the period from 2002 to 2019 (the first half of the year). The research was carried out based on the learned ANN models of the DAM system. Data were taken for examination covering the time from 2002 to 2019 (1st half of the year) and was divided into a different period, i.e., a month, a quarter, and a half-year., year, etc. The MSE, MAE, MAPE, and R2 were adopted as the criteria for assessing the ability of individual models to predict electricity prices. The research was carried out by successively expanding forecasting periods in a rolling manner. For example, for a half-year, prediction time intervals were increased from one week to month, two months, quarter, half-year, etc. results for a model representing a given period. A lot of interesting research results were obtained.
Źródło:
Studia Informatica : systems and information technology; 2022, 1(26); 5--22
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prognostics uncertainty reduction by right-time prediction of remaining useful life based on hidden Markov model and proportional hazard model
Autorzy:
Zhiyong, Gao
Jiwu, Li
Rongxi, Wang
Powiązania:
https://bibliotekanauki.pl/articles/1841766.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
RUL
uncertainty
right-time prediction
PHM
HMM
Opis:
Uncertainty is a key problem in remaining useful life (RUL) prediction, and measures to reduce uncertainty are necessary to make RUL prediction truly practical. In this paper, a right-time prediction method is proposed to reduce the prognostics uncertainty of mechanical systems under unobservable degradation. Correspondingly, the whole RUL prediction process is divided into three parts, including offline modelling, online state estimating and online life predicting. In the offline modelling part, hidden Markov model (HMM) and proportional hazard model (PHM) are built to map the whole degradation path. During operation, the degradation state of the object is estimated in real time. Once the last degradation state reached, the degradation characteristics are extracted, and the survival function is obtained with the fitted PHM. The proposed method is demonstrated on an engine dataset and shows higher accuracy than traditional method. By fusing the extracted degradation characteristics, the obtained survival function can be basis for optimal maintenance with lower uncertainty.
Źródło:
Eksploatacja i Niezawodność; 2021, 23, 1; 154-164
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prognostics uncertainty reduction by right-time prediction of remaining useful life based on hidden Markov model and proportional hazard model
Autorzy:
Zhiyong, Gao
Jiwu, Li
Rongxi, Wang
Powiązania:
https://bibliotekanauki.pl/articles/1841790.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
RUL
uncertainty
right-time prediction
PHM
HMM
Opis:
Uncertainty is a key problem in remaining useful life (RUL) prediction, and measures to reduce uncertainty are necessary to make RUL prediction truly practical. In this paper, a right-time prediction method is proposed to reduce the prognostics uncertainty of mechanical systems under unobservable degradation. Correspondingly, the whole RUL prediction process is divided into three parts, including offline modelling, online state estimating and online life predicting. In the offline modelling part, hidden Markov model (HMM) and proportional hazard model (PHM) are built to map the whole degradation path. During operation, the degradation state of the object is estimated in real time. Once the last degradation state reached, the degradation characteristics are extracted, and the survival function is obtained with the fitted PHM. The proposed method is demonstrated on an engine dataset and shows higher accuracy than traditional method. By fusing the extracted degradation characteristics, the obtained survival function can be basis for optimal maintenance with lower uncertainty.
Źródło:
Eksploatacja i Niezawodność; 2021, 23, 1; 154-164
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Prediction capabilities of the LSTM and Perceptron models based on the Day-Ahead Market on the Polish Power Exchange S.A.
Autorzy:
Ruciński, Dariusz
Powiązania:
https://bibliotekanauki.pl/articles/27323577.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
shallow networks
deep networks
Day-Ahead Market
MATLAB
Simulink environment
neural modeling
prediction time
electricity prices
Opis:
The main purpose of the research was to examine the properties of models for two kinds of neural networks, a deep learning models in which the Long Short-Term Memory was chosen and shallow neural model in which the Perceptron Neural Network was chosen. The subject of the examination was the Day-Ahead Market system of PPE S.A. The article presents the learning results of both networks and the results of the predictive abilities of the models. The research was conducted based on data published on the Polish Stock Exchange for the 2018 year. The MATLAB environment was chosen as a tool for providing the examinations. The determination index (R2) and the mean square error (MSE) was adopted as the network evaluation criterion for the learning ability and for the prediction ability of both networks.
Źródło:
Studia Informatica : systems and information technology; 2023, 1(28); 69--82
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Comparison of models for predicting thawing times of food - research note
Autorzy:
Goral, D.
Kluza, F.
Powiązania:
https://bibliotekanauki.pl/articles/1372674.pdf
Data publikacji:
1996
Wydawca:
Instytut Rozrodu Zwierząt i Badań Żywności Polskiej Akademii Nauk w Olsztynie
Tematy:
household condition
food
potato
beef
thawing time prediction
Opis:
Six most commonly known models for predicting thawing time of food: Nagaoka's et al., Cleland's et al., Calvelo's, Pham's and Piotrovich were compared when testing Tylose MH 1000 test substance, ground beef, and potato, and relative errors, regression and variance. The Cleland's et al. method, disregarding equivalent heat transfer dimensionality (EHTD) and mean conducting path (MCP) coefficient, was proved to be the best for predicting thawing time. The inclusion of EHTD and MCP to the computations by the Clelaçd's et al. method did not affect the results statistically significantly. The models of Piotrovich, Calvelo and Nagaoka et al. produced the results statistically different from real thawing times.
W pracy wykorzystano wybrane modele analityczno-empiryczne do obliczeniowego wyznaczania czasu rozmrażania żywności. Opierając się na dostępnych, wiarygodnych bazach wyników eksperymentalnych zrealizowano obliczenia dla warunków procesu charakteryzujących te bazy. Dokonano ogólnej analizy uzyskanych rezultatów wykorzystując metody statystyki matematycznej dla realizacji podstawowego celu pracy jakim była weryfikacja jakości modeli ocenianej wartością błędów obliczeń. Wyniki analizy statystycznej względnego błędu obliczeń (tabela 1) jak i analizy regresji obliczonego czasu rozmrażania względem czasu rzeczywistego (tabela 2, rysunek 1), a także badania wariancji (tabela 3) dowiodły, że trzy z sześciu testowanych modeli nie powinny być stosowane ze względu na wysokie błędy obliczeń. Jednocześnie, wyniki badań wskazują na model Clelanda i in. [1986] jako najdokładniejszy spośród badanych.
Źródło:
Polish Journal of Food and Nutrition Sciences; 1996, 05, 2; 103-108
1230-0322
2083-6007
Pojawia się w:
Polish Journal of Food and Nutrition Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Solution implementation based on modified kalman filter for purpose of bus arrival time prediction
Implementacja filtru Kalmana do prognozowania czasu przybycia autobusów
Autorzy:
Ledziński, D.
Jezierski, M.
Marciniak, B.
Marciniak, T.
Powiązania:
https://bibliotekanauki.pl/articles/389834.pdf
Data publikacji:
2011
Wydawca:
Politechnika Bydgoska im. Jana i Jędrzeja Śniadeckich. Wydawnictwo PB
Tematy:
filtr Kalmana
prognozowanie czasu
Kalman filter
time prediction
Opis:
This paper describes use of Kalman's filter for prediction of time of arrival of bus. Kalman filter is recursive algorithm determining the minimum-variance estimate of the state vector of dynamic system, based on the measurement of inputs and outputs of the system. Three prediction algorithms used: difference algorithm, traditional Kalman filter and Kalman filter with changing weights of input data. Authors studied the bus arrival time predictions. Used for this purpose data send by radio from vehicles to prediction server. The smallest average prediction error obtained for the Kalman filter with variable weights.
W pracy przedstawiono zastosowanie filtru Kalmana do prognozowania czasu przybycia autobusów. Filtr Klamana to algorytm rekurencyjnego wyznaczania minimalno-wariancyjnej estymaty wektora stanu układu dynamicznego, na podstawie pomiaru wejść i wyjść tego układu. Zbadano trzy algorytmy predykcji: algorytm różnicowy, tradycyjny filtr Kalmana oraz filtr Kalmana ze zmiennymi współczynnikami. Autorzy badali odchylenie od prognozowanego czasu przyjazdu autobusów. Używano do tego celu danych przesyłanych drogą radiową z autobusów do serwera predykcji. Najlepsze wyniki uzyskano dla filtru Kalmana ze zmiennymi współczynnikami.
Źródło:
Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy; 2011, 14; 69-78
1899-0088
Pojawia się w:
Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Community Traffic: a technology for the next generation car navigation
Autorzy:
Dembczyński, K.
Gaweł, P.
Jaszkiewicz, A.
Kotłowski, W.
Kubiak, M.
Susmaga, R.
Wesołek, P.
Wojciechowski, A.
Zielniewicz, P.
Powiązania:
https://bibliotekanauki.pl/articles/205706.pdf
Data publikacji:
2012
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
community traffic
satellite car navigation
reliability analysis
travel time prediction
Opis:
The paper presents the NaviExpert’s Community Traffic technology, an interactive, community–based car navigation system. Using data collected from its users, Community Traffic offers services unattainable to earlier systems. On the one hand, the current traffic data are used to recommend the best routes in the navigation phase, during which many potentially unpredictable traffic-delaying and traffic-jamming events, like unexpected roadworks, road accidents, or diversions, can be taken into account and thereby successfully avoided. On the other hand, a number of istinctive features, like immediate location of various traffic dangers, are offered. Using exclusively real-life data, provided by NaviExpert, the paper presents two illustrative case studies concerned with experimental evaluation of solutions to computational problems related to the community-based services offered by the system.
Źródło:
Control and Cybernetics; 2012, 41, 4; 867-883
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Analysis of spatiotemporal data to predict traffic conditions aiming at a smart navigation system for sustainable urban mobility
Autorzy:
Kyriakou, Kalliopi
Lakakis, Konstantinos
Savvaidis, Paraskevas
Basbas, Socrates
Powiązania:
https://bibliotekanauki.pl/articles/223718.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
spatio-temporal data
travel time prediction
smart navigation
urban mobility
Opis:
Urban traffic congestion created by unsustainable transport systems and considered as a crucial problem for the urbanised areas provoking air pollution, heavy economic losses due to the time and fuel wasted and social inequity. The mitigation of this problem can improve efficiency, connectivity, accessibility, safety and quality of life, which are crucial parameters of sustainable urban mobility. Encouraging sustainable urban mobility through smart solutions is essential to make the cities more liveable, sustainable and smarter. In this context, this research aims to use spatiotemporal data that taxi vehicles adequately provide, to develop an intelligent system able to predict traffic conditions and provide navigation based on these predictions. GPS (Global Positioning System) data from taxi are analysed for the case of Thessaloniki city. Trough data mining and map-matching process, the most appropriate data are selected for travel time calculations and predictions. Several algorithms are investigated to find the optimum for traffic states prediction for the specific case study concluding that ANN (Artificial Neural Networks) outperforms. Then, a new road network map is created by producing spatiotemporal models for every road segment under investigation through a linear regression implementation. Moreover, the possibility to predict vehicle emissions from travel times is investigated. Finally, an application with a graphical user interface is developed, that navigates the users with the criteria of the shortest path in terms of trip length, travel time shortest path and “eco” path. The outcome of this research is an essential tool for drivers to avoid congestion spots saving time and fuel, for stakeholders to reveal the problematic of the road network that needs amendments and for emergency vehicles to arrive at the emergency spot faster. Besides that, according to an indicator-based qualitative assessment of the proposed navigation system, it is concluded that it contributes significantly to environmental protection and economy enhancing sustainable urban mobility.
Źródło:
Archives of Transport; 2019, 52, 4; 27-46
0866-9546
2300-8830
Pojawia się w:
Archives of Transport
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Self-learning fuzzy predictor of exploitation system operating time
Autorzy:
Smoczek, J.
Szpytko, J.
Powiązania:
https://bibliotekanauki.pl/articles/247106.pdf
Data publikacji:
2011
Wydawca:
Instytut Techniczny Wojsk Lotniczych
Tematy:
operating time prediction
fuzzy logic
recursive least squares algorithm
overhead travelling crane
Opis:
The probability that a system is capable to operate satisfactorily significantly depends on reliability and maintainability of a system. The disadvantage of classic methods of system availability determining is that the probability of realizing by system tasks with expected quality depends on history of operational states and does not take into consideration actual operational conditions that have strong influence on risk-degree of down-time occurring, while the probability of degradation failure in exploitation system is a function of operating time and actual exploitation conditions. The problem of failures prediction can be solved by applying in diagnostics methods the intelligent computational algorithms. The intelligence computational methods enable to create the diagnosis tools that allow to formulate the prognosis of operating time of a system and predict of failure occurring based on the past and actual information about system's operational state. The paper presents the fuzzy logic approach to forecast the prognoses of the operating time of the exploitation system or its equipments according to the specified exploitation conditions that characterize the system exploitation state at the current time. The fuzzy system was based on the Takagi-Sugeno-Kang type fuzzy implications with singletons specifies in conclusions of rules. The fuzzy inference system input variables are the assumed parameters according to which the current exploitation state of the considered system can be evaluated.
Źródło:
Journal of KONES; 2011, 18, 4; 463-469
1231-4005
2354-0133
Pojawia się w:
Journal of KONES
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Application of long short term memory neural networks for GPS satellite clock bias prediction
Autorzy:
Gnyś, Piotr
Przestrzelski, Paweł
Powiązania:
https://bibliotekanauki.pl/articles/1987078.pdf
Data publikacji:
2021-12-30
Wydawca:
Politechnika Gdańska
Tematy:
neural networks
LSTM
time series prediction
clock bias
GNSS
machine learning
Opis:
Satellite-based localization systems like GPS or Galileo are one of the most commonly used tools in outdoor navigation. While for most applications, like car navigation or hiking, the level of precision provided by commercial solutions is satisfactory it is not always the case for mobile robots. In the case of long-time autonomy and robots that operate in remote areas battery usage and access to synchronization data becomes a problem. In this paper, a solution providing a real-time onboard clock synchronization is presented. Results achieved are better than the current state-of-the-art solution in real-time clock bias prediction for most satellites.
Źródło:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2021, 25, 4; 381-395
1428-6394
Pojawia się w:
TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Execution time prediction model for parallel GPU realizations of discrete transforms computation algorithms
Autorzy:
Puchala, Dariusz
Stokfiszewski, Kamil
Wieloch, Kamil
Powiązania:
https://bibliotekanauki.pl/articles/2173530.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
graphics processing unit
GPU
execution time prediction model
discrete wavelet transform
DWT
lattice structure
convolution-based approach
orthogonal transform
orthogonal filter banks
time effectiveness
prediction accuracy
Opis:
Parallel realizations of discrete transforms (DTs) computation algorithms (DTCAs) performed on graphics processing units (GPUs) play a significant role in many modern data processing methods utilized in numerous areas of human activity. In this paper the authors propose a novel execution time prediction model, which allows for accurate and rapid estimation of execution times of various kinds of structurally different DTCAs performed on GPUs of distinct architectures, without the necessity of conducting the actual experiments on physical hardware. The model can serve as a guide for the system analyst in making the optimal choice of the GPU hardware solution for a given computational task involving particular DT calculation, or can help in choosing the best appropriate parallel implementation of the selected DT, given the limitations imposed by available hardware. Restricting the model to exhaustively adhere only to the key common features of DTCAs enables the authors to significantly simplify its structure, leading consequently to its design as a hybrid, analytically–simulational method, exploiting jointly the main advantages of both of the mentioned techniques, namely: time-effectiveness and high prediction accuracy, while, at the same time, causing mutual elimination of the major weaknesses of both of the specified approaches within the proposed solution. The model is validated experimentally on two structurally different parallel methods of discrete wavelet transform (DWT) computation, i.e. the direct convolutionbased and lattice structure-based schemes, by comparing its prediction results with the actual measurements taken for 6 different graphics cards, representing a fairly broad spectrum of GPUs compute architectures. Experimental results reveal the overall average execution time and prediction accuracy of the model to be at a level of 97.2%, with global maximum prediction error of 14.5%, recorded throughout all the conducted experiments, maintaining at the same time high average evaluation speed of 3.5 ms for single simulation duration. The results facilitate inferring the model generality and possibility of extrapolation to other DTCAs and different GPU architectures, which along with the proposed model straightforwardness, time-effectiveness and ease of practical application, makes it, in the authors’ opinion, a very interesting alternative to the related existing solutions.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 1; e139393, 1--30
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A deep ensemble learning method for effort-aware just-in-time defect prediction
Autorzy:
Albahli, Saleh
Powiązania:
https://bibliotekanauki.pl/articles/117652.pdf
Data publikacji:
2020
Wydawca:
Polskie Towarzystwo Promocji Wiedzy
Tematy:
Deep Neural Network
unlabeled dataset
Just-In-Time defect prediction
unsupervised prediction
nieoznakowany zbiór danych
przewidywanie defektów Just-In-Time
przewidywanie bez nadzoru
Opis:
Since the introduction of Just-in-Time effort aware defect prediction, many researchers are focusing on evaluating the different learning methods for defect prediction. To predict the changes that are defect-inducing, it is im-portant for learning model to consider the nature of the dataset, its imbalance properties and the correlation between different attributes. In this paper, we evaluated the importance of dataset properties, and proposed a novel methodology for learning the effort aware just-in-time defect prediction model. We form an ensemble classifier, which consider the output of three individuals classifier i.e. Random forest, XGBoost and Deep Neural Network. Our proposed methodology shows better performance with 77% accuracy on sample dataset and 81% accuracy on different dataset.
Źródło:
Applied Computer Science; 2020, 16, 3; 5-15
1895-3735
Pojawia się w:
Applied Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Some aspects of Intelligent Transport System auditing
Autorzy:
Bazan, M.
Bożek, M.
Ciskowski, P.
Halawa, K.
Janiczek, T.
Kozaczewski, P.
Rusiecki, A.
Powiązania:
https://bibliotekanauki.pl/articles/393760.pdf
Data publikacji:
2015
Wydawca:
Polskie Stowarzyszenie Telematyki Transportu
Tematy:
intelligent transportation
system audit
travel time prediction
fundamental diagram
inteligentny transport
audit systemu
przewidywanie czasu podróży
diagram fundamentalny
Opis:
Nowadays, in urbanized areas one of the most important matters is to determine a priori the time of driving from one zone of the city to another at various times of the day. The problem of travel time prediction is crucial in Intelligent Transportation Systems. The solution to this problem is a foundation of any route guidance system that will redirect drivers to their target destination via routes that have a lighter traffic load and thus higher travel velocity. In this paper is present a concept of a statistical methodology, developed by the ArsNumerica Group, that enables a quantity audit a travel time prediction algorithm. The methodology assumes that we are given database records of vehicles recognized by their unique identifier as well as duration times for which the messages with the predicted travel time are displayed VMS. the second aspect of ITS auditing considered in this paper is a placement of video cameras to measure vehicle stream velocity. Inappropriate camera location results in the fact that the stream velocity measured by them has a low usefulness for travel time prediction.
Źródło:
Archives of Transport System Telematics; 2015, 8, 3; 3-8
1899-8208
Pojawia się w:
Archives of Transport System Telematics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Quantum road traffic model for ambulance travel time estimation
Autorzy:
Bernas, M.
Wisniewska, J.
Powiązania:
https://bibliotekanauki.pl/articles/333463.pdf
Data publikacji:
2013
Wydawca:
Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:
ambulance travel time prediction
quantum processing
traffic modelling
przewidywany czas jazdy pogotowia
przetwarzanie informacji kwantowej
modelowanie ruchu drogowego
Opis:
Efficient management of ambulance utilisation is a vital issue for life saving. Knowledge of the amount of time needed for an ambulance to get to the hospital and when it will be available for a new task, can be estimated using modern Intelligent Transport Systems. Their main feature is an ability to simulate the state of traffic not only in long term, but also the real time events like accidents or high congestion, using microscopic models. The paper introduces usage of Quantum Computing paradigm to propose a quantum model of road traffic, which can track the state of traffic and estimate the travel time of vehicles. Model, if run on quantum computer can simulate the traffic in vast areas in real time. Proposed model was verified against the cellular automata model. Finally, application of quantum microscopic traffic models for ambulance vehicles was taken into consideration.
Źródło:
Journal of Medical Informatics & Technologies; 2013, 22; 257-264
1642-6037
Pojawia się w:
Journal of Medical Informatics & Technologies
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Data mining workspace as an optimization prediction technique for solving transport problems
Решение задачи прогнозирования в транспортной отрасли с помощью методов data mining
Autorzy:
Kuptcova, A.
Průša, P.
Federko, G.
Molnár, V.
Powiązania:
https://bibliotekanauki.pl/articles/375552.pdf
Data publikacji:
2016
Wydawca:
Politechnika Śląska. Wydawnictwo Politechniki Śląskiej
Tematy:
time series prediction
data mining
neural network
modelling
predykcja szeregów czasowych
eksploracja danych
sieć neuronowa
modelowanie
Opis:
This article addresses the study related to forecasting with an actual high-speed decision making under careful modelling of time series data. The study uses data-mining modelling for algorithmic optimization of transport goals. Our finding brings to the future adequate techniques for the fitting of a prediction model. This model is going to be used for analyses of the future transaction costs in the frontiers of the Czech Republic. Time series prediction methods for the performance of prediction models in the package of Statistics are Exponential, ARIMA and Neural Network approaches. The primary target for a predictive scenario in the data mining workspace is to provide modelling data faster and with more versatility than the other management techniques.
В данной статье рассматривается задача прогнозирования временных рядов, которая заключается в построении модели для предсказания будущих событий. В исследовании используются методы интеллектуального анализа данных. Модель прогнозирования позволяет адекватно оценивать исследуемый процесс. Целью исследования является изучение динамики расходов при реализации экспортной продукции. Прогнозирование осуществляется с помощью ARIMA-модели, на основе метода экспоненциального сглаживания и по технологии логической нейронной сети. Построение базового и быстрого сценария прогнозирования является важным и ответственным этапом в научной деятельности.
Źródło:
Transport Problems; 2016, 11, 3; 21-31
1896-0596
2300-861X
Pojawia się w:
Transport Problems
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 49

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