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Wyświetlanie 1-8 z 8
Tytuł:
Examination of Seasonal Volatility in HICP for Baltic Region Countries: Non-Parametric Test versus Forecasting Experiment
Autorzy:
Lenart, Łukasz
Powiązania:
https://bibliotekanauki.pl/articles/2076445.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
HICP
seasonal volatility
exponential smoothing
nowcasting
predictive distribution
logscore
Opis:
The aim of this paper is to examine the problem of existing seasonal volatility in total and disaggregated HICP for Baltic Region countries (Denmark, Estonia, Latvia, Finland, Germany, Lithuania, Poland and Sweden). Using nonparametric tests, we found that in the case of m-o-m prices, including fruit, vegetables, and total HICP, the homogeneity of variance during seasons is rejected. Based on these findings, we propose an exponential smoothing model with periodic variance of error terms that capture the repetitive seasonal variation (in conditional or unconditional second moments). In a pseudo-real data experiment, the short-term forecasts (nowcasting) for the considered components of inflation were determined using different specifications of considered models. The forecasting performance of the models was measured using one of the scoring rules for probabilistic forecasts called logarithmic score. We found instead that while the periodic phenomenon in variance was statistically significant, the models with a periodic phenomenon in variance of error terms do not significantly improve forecasting performance in disaggregated cases and in the case of total HICP. The simpler models with constant variance of error term have comparative forecasting (nowcasting) performance over the alternative model
Źródło:
Central European Journal of Economic Modelling and Econometrics; 2017, 1; 29-67
2080-0886
2080-119X
Pojawia się w:
Central European Journal of Economic Modelling and Econometrics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Bayesian Inference for State Space Model with Panel Data
Autorzy:
Pandey, Ranjita
Chaturvedi, Anoop
Powiązania:
https://bibliotekanauki.pl/articles/466044.pdf
Data publikacji:
2016
Wydawca:
Główny Urząd Statystyczny
Tematy:
Bayesian analysis
Gibbs sampler
conditional posterior densities
predictive distribution
Opis:
The present work explores panel data set-up in a Bayesian state space model. The conditional posterior densities of parameters are utilized to determine the marginal posterior densities using the Gibbs sampler. An efficient one step ahead predictive density mechanism is developed to further the state of art in prediction-based decision making.
Źródło:
Statistics in Transition new series; 2016, 17, 2; 211-220
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Combining predictive distributions of electricity prices : does minimizing the CRPS lead to optimal decisions in day-ahead bidding?
Autorzy:
Nitka, Weronika
Weron, Rafał
Powiązania:
https://bibliotekanauki.pl/articles/27315321.pdf
Data publikacji:
2023
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
decision support
day-ahead electricity bidding
predictive distribution
combining forecast
CRPS learning
Opis:
Probabilistic price forecasting has recently gained attention in power trading because decisions based on such predictions can yield significantly higher profits than those made with point forecasts alone. At the same time, methods are being developed to combine predictive distributions, since no model is perfect and averaging generally improves forecasting performance. In this article, we address the question of whether using CRPS learning, a novel weighting technique minimizing the continuous ranked probability score (CRPS), leads to optimal decisions in day-ahead bidding. To this end, we conduct an empirical study using hourly day-ahead electricity prices from the German EPEX market. We find that increasing the diversity of an ensemble can have a positive impact on accuracy. At the same time, the higher computational cost of using CRPS learning compared to an equal-weighted aggregation of distributions is not offset by higher profits, despite significantly more accurate predictions.
Źródło:
Operations Research and Decisions; 2023, 33, 3; 105--118
2081-8858
2391-6060
Pojawia się w:
Operations Research and Decisions
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Predykcyjne sterowanie ciśnieniem sieci wodociągowej w celu optymalizacji parametrów jej pracy
Predictive pressure control of a water distribution network
Autorzy:
Wiglenda, R.
Moczulski, W.
Powiązania:
https://bibliotekanauki.pl/articles/152757.pdf
Data publikacji:
2011
Wydawca:
Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Tematy:
sterowanie predykcyjne
Model Predictive Control
MPC
sterowanie ciśnieniem w sieci wodociągowej
predictive control
model predictive control
pressure control of a water distribution network
Opis:
W artykule zaprezentowano wyniki badań dotyczących sterowania predykcyjnego ciśnieniem w sieci wodociągowej o wielu zasilaniach. Przedmiotem sterowania była optymalizacja parametrów pracy sieci, mająca na celu obniżenie ciśnienia w porze nocnej, w miejscach sieci, gdzie ze względu na ich położenie ponad poziomem morza występuje maksymalne ciśnienie, przy jednoczesnym zapewnieniu minimalnego wymaganego ciśnienia w pozostałych punktach sieci. Rezultat ten uzyskano przez manipulowanie ciśnieniami na zasilaniach, które daje pożądany przebieg linii ciśnień dla danej strefy. W budowie regulatora zastosowano algorytm Generalized Predictive Control (GPC), w którym model obiektu sterowanego zidentyfikowano w oparciu o dane otrzymane w wyniku eksperymentu numerycznego. Sprawdzenie poprawności rozwiązania przeprowadzono w środowisku Matlab® korzystając z przybornika Model Predictive Control™. Weryfikację działania metody oparto o dane i strukturę obiektu zidentyfikowane w ramach projektu prowadzonego we współpracy z PWiK Rybnik Sp. z. o. o. Otrzymane wyniki potwierdzają poprawność działania metody dla tak postawionego problemu eksploatacji sieci wodociągowych.
The paper presents results of research on predictive control of a water distribution network with multiple supplies. The object of study was optimization of work parameters of a water distribution network in order to decrease the level of pressure at night for the places with the highest pressure values. This was achieved by manipulating the supply pressures to obtain the desired level of the pressure lines for a given area. The considered water distribution networks are shown in Figs. 1 and 2, whereas description of the network characteristic points is given in Tab. 1. The detailed discussion of selected water networks is contained in [1, 2, 3]. The authors made a deep literature review [4] and, as a result, selected the Generalized Predictive Control algorithm (see Fig. 4) as a framework for the predictive control. The schema of the predictive control for an exemplary water distribution network is shown in Fig. 5. The con-trolled plant was identified from a numerical experiment made on a network simulation model. An exemplary result of model identification is shown in Fig. 3. The solution was verified in the Matlab® environment with use of the Model Predictive Control™ toolbox. The results shown in Figs. 6-10 confirm that the method is a proper solution to this water distribution network maintenance problem. Implementation of the predictive control in a real water distribution network needs some costs to be incurred. However, it is expected that the costs will be returned soon, thanks to significant decrease in the number of malfunctions in the network caused by excessive hydraulic loads of the water network elements. In addition, it is possible to use the predictive control as a solution to the problem of undetected leakages whose level may be reduced with this method of control.
Źródło:
Pomiary Automatyka Kontrola; 2011, R. 57, nr 9, 9; 1028-1031
0032-4140
Pojawia się w:
Pomiary Automatyka Kontrola
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A combinatorial approach in predicting the outcome of tennis matches
Autorzy:
Šarčević, Ana
Vranić, Mihaela
Pintar, Damir
Powiązania:
https://bibliotekanauki.pl/articles/2055160.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
binomial distribution
final score prediction
independent distribution
identical distribution
predictive model
rozkład dwumianowy
przewidywanie wyniku końcowego
rozkład identyczny
model predykcji
Opis:
Tennis, as one of the most popular individual sports in the world, holds an important role in the betting world. There are two main categories of bets: pre-match betting, which is conducted before the match starts, and live betting, which allows placing bets during the sporting event. Betting systems rely on setting sports odds, something historically done by domain experts. Setting odds for live betting represents a challenge due to the need to follow events in real-time and react accordingly. In tennis, hierarchical models often stand out as a popular choice when trying to predict the outcome of the match. These models commonly leverage a recursive approach that aims to predict the winner or the final score starting at any point in the match. However, recursive expressions inherently contain computational complexity which hinders the efficiency of methods relying on them. This paper proposes a more resource-effective alternative in the form of a combinatorial approach based on a binomial distribution. The resulting accuracy of the combinatorial approach is identical to that of the recursive approach while being vastly more efficient when considering the execution time, making it a superior choice for live betting in this domain.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 3; 525--538
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Optimizing control by robustly feasible model predictive control and application to drinkingwater distribution systems
Autorzy:
Tran, V. N.
Brdys, M. A.
Powiązania:
https://bibliotekanauki.pl/articles/91723.pdf
Data publikacji:
2011
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
optimizing
model predictive control
MPC
Robustly Feasible MPC
RFMPC
Drinking
Water Distribution Systems
DWDS
genetic algorithm
Opis:
The paper considers optimizing Model Predictive Control (MPC) for nonlinear plants with output constraints under uncertainties. Although the MPC technology can handle the constraints in the model by solving constraint model based optimization task, satisfying the plant output constraints under the model uncertainty still remains a challenge. The paper proposes Robustly Feasible MPC (RFMPC), which achieves feasibility of the outputs in the controlled plant. The RFMPC which is applied to control quantity in Drinking Water Distribution Systems (DWDS) is illustrated by application to the DWDS example. In the simulation exercise, Genetic Algorithm is selected as the optimization solver and the reduced search space methodology is applied in the implementation under MATLABEPANET environment.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 1; 43-57
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Driving energy management of front-and-rear-motor-drive electric vehicle based on hybrid radial basis function
Autorzy:
Sun, Binbin
Zhang, Tiezhu
Ge, Wenqing
Tan, Cao
Gao, Song
Powiązania:
https://bibliotekanauki.pl/articles/224152.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
electric vehicle
drive
energy management
optimization
torque distribution
predictive model
hardware test
pojazd elektryczny
napęd
zarządzanie energią
optymalizacja
moment obrotowy
model predykcyjny
Opis:
This paper presents mathematical methods to develop a high-efficiency and real-time driving energy management for a front-and-rear-motor-drive electric vehicle (FRMDEV), which is equipped with an induction motor (IM) and a permanent magnet synchronous motor (PMSM). First of all, in order to develop motor-loss models for energy optimization, database of with three factors, which are speed, torque and temperature, was created to characterize motor operation based on HALTON sequence method. The response surface model of motor loss, as the function of the motor-operation database, was developed with the use of Gauss radial basis function (RBF). The accuracy of the motor-loss model was verified according to statistical analysis. Then, in order to create a two-factor energy management strategy, the modification models of the torque required by driver (Td) and the torque distribution coefficient (β) were constructed based on the state of charge (SOC) of battery and the motor temperature, respectively. According to the motor-loss models, the fitness function for optimization was designed, where the influence of the non-work on system consumption was analyzed and calculated. The optimal β was confirmed with the use of the off-line particle swarm optimization (PSO). Moreover, to achieve both high accuracy and real-time performance under random vehicle operation, the predictive model of the optimal β was developed based on the hybrid RBF. The modeling and predictive accuracies of the predictive model were analyzed and verified. Finally, a hardware-in-loop (HIL) test platform was developed and the predictive model was tested. Test results show that, the developed predictive model of β based on hybrid RBF can achieve both real-time and economic performances, which is applicable to engineering application. More importantly, in comparison with the original torque distribution based on rule algorithm, the torque distribution based on hybrid RBF is able to reduce driving energy consumption by 9.51% under urban cycle.
Źródło:
Archives of Transport; 2019, 49, 1; 47-58
0866-9546
2300-8830
Pojawia się w:
Archives of Transport
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Modelling the distribution performance in dairy industry: a predictive analysis
Autorzy:
Mor, Rahul S.
Bhardwaj, Arvind
Singh, Sarbjit
Khan, Syed Abdul Rehman
Powiązania:
https://bibliotekanauki.pl/articles/1835473.pdf
Data publikacji:
2021
Wydawca:
Wyższa Szkoła Logistyki
Tematy:
distribution performance
food supply chain
dairy industry
structural equation modelling
SEM
predictive analysis
wydajność dystrybucji
łańcuch dostaw żywności
przemysł mleczarski
modelowanie równań strukturalnych
analiza predykcyjna
Opis:
Predictive analysis is a vital element to operations management as it facilitates real-time decision making and advanced planning on both strategy and performance. This paper identifies predictors to measure distribution performance in the dairy industry and to establish their importance. Methods: A distribution model is developed through exploratory structural equation modelling (SEM) techniques. The key performance predictors are marketing and distribution management, quality management, supply chain coordination, and brand management, which account for 71.5% of the variability in distribution performance. Results and conclusion: The predictors help improving the distribution performance, specifically in quality, order fill rate, and food safety. The outcomes of this research can help dairy professionals in managing their distribution channels, improving traceability, on-time delivery, and shipment accuracy. Consequently, these factors can improve distribution performance. Four predictors are elicited from the data to estimate the distribution performance and the relative importance of predictors is also established.
Źródło:
LogForum; 2021, 17, 3; 425-440
1734-459X
Pojawia się w:
LogForum
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-8 z 8

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