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Time Series Forecasting Using Statistical And Neural Networks Models - Softcover

 
9783659944741: Time Series Forecasting Using Statistical And Neural Networks Models

Inhaltsangabe

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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Reseña del editor

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.

Biografía del autor

Abdoulaye Camara, Master Department of Information and Computing Science, School of Mathematics and Physics,University of Science and Technology Beijing, China

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  • VerlagLAP LAMBERT Academic Publishing
  • Erscheinungsdatum2016
  • ISBN 10 3659944742
  • ISBN 13 9783659944741
  • EinbandTapa blanda
  • SpracheEnglisch
  • Anzahl der Seiten120
  • Kontakt zum HerstellerNicht verfügbar

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Abdoulaye Camara
ISBN 10: 3659944742 ISBN 13: 9783659944741
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Taschenbuch. Zustand: 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. Artikel-Nr. 9783659944741

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Camara, Abdoulaye
ISBN 10: 3659944742 ISBN 13: 9783659944741
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Paperback. Zustand: Brand New. 120 pages. 8.66x5.91x0.28 inches. In Stock. Artikel-Nr. 3659944742

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