Time Series Forecasting Using Statistical And Neural Networks Models

Abdoulaye Camara

ISBN 10: 3659944742 / ISBN 13: 9783659944741

Verlag: LAP LAMBERT Academic Publishing Sep 2016, 2016

Beschreibung

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. Bestandsnummer des Verkäufers 9783659944741

Bibliografische Details

Titel: Time Series Forecasting Using Statistical ...

Verlag: LAP LAMBERT Academic Publishing Sep 2016

Erscheinungsdatum: 2016

Einband: Taschenbuch

Zustand: Neu

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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.