Temat: short-term forecasting - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Forecasting GDP growth rate in Ukraine with alternative models
Autorzy:: Karayuz, I
Bidyuk, P.
Powiązania:: https://bibliotekanauki.pl/articles/118047.pdf
Data publikacji:: 2015
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Tematy:: Regressive model
Bayesian network
short-term forecasting
GDP of Ukraine
Opis:: The problem of constructing mathematical model for short-term fore-casting of GDP is considered. First, extended autoregression is constru-cted that takes two additional independent variables into consideration. The model resulted provides a possibility for generating short-term forecasts of GDP though not of high quality. Another model was constructed in the form of a Bayesian network. The model turned out to be better than the multiple regression, it provides quite good estimates for probabilities of GDP growth direction.
Źródło:: Applied Computer Science; 2015, 11, 3; 88-97
1895-3735
Pojawia się w:: Applied Computer Science
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: An overview of deep learning techniques for short-term electricity load forecasting
Autorzy:: Adewuyi, Saheed
Aina, Segun
Uzunuigbe, Moses
Lawal, Aderonke
Oluwaranti, Adeniran
Powiązania:: https://bibliotekanauki.pl/articles/117932.pdf
Data publikacji:: 2019
Wydawca:: Polskie Towarzystwo Promocji Wiedzy
Tematy:: Short-term Load Forecasting
Deep Learning Architectures
RNN
LSTM
CNN
SAE
prognozowanie obciążenia krótkoterminowego
architektura głębokiego uczenia
Opis:: This paper presents an overview of some Deep Learning (DL) techniques applicable to forecasting electricity consumptions, especially in the short-term horizon. The paper introduced key parts of four DL architectures including the RNN, LSTM, CNN and SAE, which are recently adopted in implementing Short-term (electricity) Load Forecasting problems. It further presented a model approach for solving such problems. The eventual implication of the study is to present an insightful direction about concepts of the DL methods for forecasting electricity loads in the short-term period, especially to a potential researcher in quest of solving similar problems.
Źródło:: Applied Computer Science; 2019, 15, 4; 75-92
1895-3735
Pojawia się w:: Applied Computer Science
Dostawca treści:: Biblioteka Nauki

Artykuł

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