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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.
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- EditoreLAP LAMBERT Academic Publishing
- Data di pubblicazione2016
- ISBN 10 3659944742
- ISBN 13 9783659944741
- RilegaturaCopertina flessibile
- LinguaInglese
- Numero di pagine120
- Contatto del produttorenon disponibile
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Time Series Forecasting Using Statistical And Neural Networks Models
Editore:
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659944742
ISBN 13: 9783659944741
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Da: moluna, Greven, Germania
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Condizione: 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. Codice articolo 153342469
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Time Series Forecasting Using Statistical And Neural Networks Models
Editore:
LAP LAMBERT Academic Publishing Sep 2016, 2016
ISBN 10: 3659944742
ISBN 13: 9783659944741
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: 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. Codice articolo 9783659944741
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Time Series Forecasting Using Statistical And Neural Networks Models
Editore:
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659944742
ISBN 13: 9783659944741
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: 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. Codice articolo 9783659944741
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Time Series Forecasting Using Statistical And Neural Networks Models
Editore:
LAP LAMBERT Academic Publishing Sep 2016, 2016
ISBN 10: 3659944742
ISBN 13: 9783659944741
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 120 pp. Englisch. Codice articolo 9783659944741
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Time Series Forecasting Using Statistical And Neural Networks Models
Editore:
LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659944742
ISBN 13: 9783659944741
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Paperback. Condizione: Brand New. 120 pages. 8.66x5.91x0.28 inches. In Stock. Codice articolo 3659944742
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