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Refined Neural Network for Time Seri...

Adnan, Mian Arif Shams,

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Refined Neural Network for Time Series Predictions /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Refined Neural Network for Time Series Predictions // Mian Arif Shams Adnan.
Author:	Adnan, Mian Arif Shams,
Description:	1 electronic resource (150 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
Contained By:	Dissertations Abstracts International86-03B.
Subject:	Statistics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31604019
ISBN:	9798383685389

Refined Neural Network for Time Series Predictions /
Adnan, Mian Arif Shams,

Refined Neural Network for Time Series Predictions /Mian Arif Shams Adnan. - 1 electronic resource (150 pages)

Source: Dissertations Abstracts International, Volume: 86-03, Section: B.

Deep learning, neural network, has been penetrating into almost every corner of data analyses. With advantages on computing power and speed, adding more layers in a neural network analysis becomes a common practice to improve the prediction accuracy. However, over depleting information in the training dataset may consequently carry data noises into the learning process of neural network and result in over-fitting errors. Neural Network has been used to predict the future time series data. It had been claimed by several authors that the Neural Network (Recurrent Neural Network) can predict the time series data, although time series models have also been used to predict the future time series data. This dissertation is thus motivated to investigate the prediction performances of neural networks versus the conventional inference method of time series analysis.After introducing basic concepts and theoretical background of neural network and time series prediction in Chapter 1, Chapter 2 analyzes fundamental structure of time series, along with estimation, hypothesis testing, and prediction methods. Chapter 3 discusses details of computing algorithms and procedures in neural network with theoretical adjustment for time series prediction. In conjunction with terminologies and methodologies in the previous chapters, Chapter 4 directly compares the prediction results of neural networks and conventional time series for the squared error function. In terms of methodology assessment, the evaluation criterion plays a critical role. The performance of the existing neural network models for time series predictions has been observed. It has been experimentally observed that the time series predictions by time series models are better compared to the neural network models both computationally and theoretically. The conditions for the better performances of the Time Series Models over the Neural Network Models have been discovered. Theorems have also been proved to demonstrate the superiority of Time Series Modeling over Neural Network Modeling. Chapter 5 summarizes the conclusions and research results in the dissertation and describes future research topics in this direction.In summary, the dissertation discovers that when the underlying model is unknown or cannot be specified, the neural network provides more accurate prediction results. However, when the underlying assumptions of Auto-regressive Integrated Moving Average or ARIMA (2,0,0) or AR(2) Model can be satisfied either by cross-validation or by background knowledge of the data, the conventional time series approaches perform more accurately, as backed up by underlying theoretical proofs and extensive simulation results.

English

ISBN: 9798383685389Subjects--Topical Terms:

517247
Statistics.
Subjects--Index Terms:

Auto-regressive integrated moving average model

Refined Neural Network for Time Series Predictions /
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520 $a Deep learning, neural network, has been penetrating into almost every corner of data analyses. With advantages on computing power and speed, adding more layers in a neural network analysis becomes a common practice to improve the prediction accuracy. However, over depleting information in the training dataset may consequently carry data noises into the learning process of neural network and result in over-fitting errors. Neural Network has been used to predict the future time series data. It had been claimed by several authors that the Neural Network (Recurrent Neural Network) can predict the time series data, although time series models have also been used to predict the future time series data. This dissertation is thus motivated to investigate the prediction performances of neural networks versus the conventional inference method of time series analysis.After introducing basic concepts and theoretical background of neural network and time series prediction in Chapter 1, Chapter 2 analyzes fundamental structure of time series, along with estimation, hypothesis testing, and prediction methods. Chapter 3 discusses details of computing algorithms and procedures in neural network with theoretical adjustment for time series prediction. In conjunction with terminologies and methodologies in the previous chapters, Chapter 4 directly compares the prediction results of neural networks and conventional time series for the squared error function. In terms of methodology assessment, the evaluation criterion plays a critical role. The performance of the existing neural network models for time series predictions has been observed. It has been experimentally observed that the time series predictions by time series models are better compared to the neural network models both computationally and theoretically. The conditions for the better performances of the Time Series Models over the Neural Network Models have been discovered. Theorems have also been proved to demonstrate the superiority of Time Series Modeling over Neural Network Modeling. Chapter 5 summarizes the conclusions and research results in the dissertation and describes future research topics in this direction.In summary, the dissertation discovers that when the underlying model is unknown or cannot be specified, the neural network provides more accurate prediction results. However, when the underlying assumptions of Auto-regressive Integrated Moving Average or ARIMA (2,0,0) or AR(2) Model can be satisfied either by cross-validation or by background knowledge of the data, the conventional time series approaches perform more accurately, as backed up by underlying theoretical proofs and extensive simulation results.
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653 $a Back propagation
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