Forecasting is a common statistical task in many areas, where it contributes to inform decisions about the scheduling of production, transportation, personnel, etc. And it provides a guide to long-term strategic planning. In many areas such as financial, energy, economics, the time series data are non-stationary, contain trend and seasonal variations. The goal of this thesis is to forecast the time series using two approaches, namely the statistical approaches; they are seasonal ARIMA, seasonal VARIMA models and Neural Networks approach and compare them in order to find the best model for time series forecasting. The energy area has an important role in the development of countries; thus, consumption planning of energy must be made accurately, despite they are governed by other factors such as that population, gross domestic product, weather vagaries, storage capacity, etc.
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Forecasting is a common statistical task in many areas, where it contributes to inform decisions about the scheduling of production, transportation, personnel, etc. And it provides a guide to long-term strategic planning. In many areas such as financial, energy, economics, the time series data are non-stationary, contain trend and seasonal variations. The goal of this thesis is to forecast the time series using two approaches, namely the statistical approaches; they are seasonal ARIMA, seasonal VARIMA models and Neural Networks approach and compare them in order to find the best model for time series forecasting. The energy area has an important role in the development of countries; thus, consumption planning of energy must be made accurately, despite they are governed by other factors such as that population, gross domestic product, weather vagaries, storage capacity, etc.
Abdoulaye Camara, Master Department of Information and Computing Science, School of Mathematics and Physics,University of Science and Technology Beijing, China
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Forecasting is a common statistical task in many areas, where it contributes to inform decisions about the scheduling of production, transportation, personnel, etc. And it provides a guide to long-term strategic planning. In many areas such as financial, energy, economics, the time series data are non-stationary, contain trend and seasonal variations. The goal of this thesis is to forecast the time series using two approaches, namely the statistical approaches; they are seasonal ARIMA, seasonal VARIMA models and Neural Networks approach and compare them in order to find the best model for time series forecasting. The energy area has an important role in the development of countries; thus, consumption planning of energy must be made accurately, despite they are governed by other factors such as that population, gross domestic product, weather vagaries, storage capacity, etc. 120 pp. Englisch. N° de réf. du vendeur 9783659944741
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Camara AbdoulayeAbdoulaye Camara, Master Department of Information and Computing Science, School of Mathematics and Physics,University of Science and Technology Beijing, ChinaForecasting is a common statistical task in many areas. N° de réf. du vendeur 153342469
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Forecasting is a common statistical task in many areas, where it contributes to inform decisions about the scheduling of production, transportation, personnel, etc. And it provides a guide to long-term strategic planning. In many areas such as financial, energy, economics, the time series data are non-stationary, contain trend and seasonal variations. The goal of this thesis is to forecast the time series using two approaches, namely the statistical approaches; they are seasonal ARIMA, seasonal VARIMA models and Neural Networks approach and compare them in order to find the best model for time series forecasting. The energy area has an important role in the development of countries; thus, consumption planning of energy must be made accurately, despite they are governed by other factors such as that population, gross domestic product, weather vagaries, storage capacity, etc.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 120 pp. Englisch. N° de réf. du vendeur 9783659944741
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Forecasting is a common statistical task in many areas, where it contributes to inform decisions about the scheduling of production, transportation, personnel, etc. And it provides a guide to long-term strategic planning. In many areas such as financial, energy, economics, the time series data are non-stationary, contain trend and seasonal variations. The goal of this thesis is to forecast the time series using two approaches, namely the statistical approaches; they are seasonal ARIMA, seasonal VARIMA models and Neural Networks approach and compare them in order to find the best model for time series forecasting. The energy area has an important role in the development of countries; thus, consumption planning of energy must be made accurately, despite they are governed by other factors such as that population, gross domestic product, weather vagaries, storage capacity, etc. N° de réf. du vendeur 9783659944741
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Taschenbuch. Etat : Neu. Time Series Forecasting Using Statistical And Neural Networks Models | Abdoulaye Camara | Taschenbuch | 120 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659944741 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. N° de réf. du vendeur 103010180
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