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Applications Uni Variate Time by Ravi Ramakrishna (4 results)
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Applications of UNI Variate Time Series Models and Neural Networks
Published by LAP LAMBERT Academic Publishing Mär 2020, 2020
ISBN 10: 620056857X ISBN 13: 9786200568571
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
US$ 74.63
Convert currencyUS$ 70.23 shipping from Germany to U.S.A.Quantity: 2 available
Add to basketTaschenbuch. Condition: Neu. Neuware -Forecasting of daily and monthly electricity load using Box-Jenkins methodology and feed forward neural networks is discussed. This study investigates application of neural networks models and the results of neural networks determination be compared with those obtained by Box-Jenkins method. The performances were compared based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The Final conclusion of this book is Feed-Forward neural networks models are better and superior than Box-Jenkins models.Books on Demand GmbH, Überseering 33, 22297 Hamburg 140 pp. Englisch.
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Applications of UNI Variate Time Series Models and Neural Networks
Published by LAP LAMBERT Academic Publishing Mrz 2020, 2020
ISBN 10: 620056857X ISBN 13: 9786200568571
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
US$ 74.63
Convert currencyUS$ 26.92 shipping from Germany to U.S.A.Quantity: 2 available
Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Forecasting of daily and monthly electricity load using Box-Jenkins methodology and feed forward neural networks is discussed. This study investigates application of neural networks models and the results of neural networks determination be compared with those obtained by Box-Jenkins method. The performances were compared based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The Final conclusion of this book is Feed-Forward neural networks models are better and superior than Box-Jenkins models. 140 pp. Englisch.
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Applications of UNI Variate Time Series Models and Neural Networks
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 620056857X ISBN 13: 9786200568571
Language: English
US$ 61.08
Convert currencyUS$ 57.34 shipping from Germany to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ravi RamakrishnaDr. Ravi.Ramakrishna is a Professor in Statistics VJIT (A), Hyderabad. He has completed Ph.D. (Statistics) from Osmania University. He has more than 14 years of experience in VJIT,Hyderabad and 4 years of experience.
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Applications of UNI Variate Time Series Models and Neural Networks : Forecasting of Electricity Load in Andhra Pradesh using Neural Network
Published by LAP LAMBERT Academic Publishing, 2020
ISBN 10: 620056857X ISBN 13: 9786200568571
Language: English
US$ 75.52
Convert currencyUS$ 71.56 shipping from Germany to U.S.A.Quantity: 1 available
Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Forecasting of daily and monthly electricity load using Box-Jenkins methodology and feed forward neural networks is discussed. This study investigates application of neural networks models and the results of neural networks determination be compared with those obtained by Box-Jenkins method. The performances were compared based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The Final conclusion of this book is Feed-Forward neural networks models are better and superior than Box-Jenkins models.
