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Introduction

The use of neural networks for time series prediction has gained significant attention in recent years due to its ability to capture complex patterns and relationships within data. This thesis focuses on building a neural network model for time series prediction, with the aim of improving upon existing methods and achieving accurate forecasts.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Overview of time series prediction
2.2 Traditional methods for time series prediction
2.3 Neural networks for time series prediction
2.4 Deep learning for time series prediction
2.5 Challenges in time series prediction
2.6 Applications of time series prediction
2.7 Comparative analysis of existing models
2.8 Recent advancements in neural network models
2.9 Gaps in the literature
2.10 Summary of literature review

Chapter 3: System Design and Methodology
3.1 Data collection and preprocessing
3.2 Feature selection and engineering
3.3 Model selection and architecture
3.4 Training and optimization
3.5 Performance evaluation metrics
3.6 Hyperparameter tuning
3.7 Cross-validation techniques
3.8 Implementation of the neural network model
3.9 Validation and testing

Chapter 4: System Implementation
4.1 Dataset description
4.2 Data preprocessing steps
4.3 Feature engineering process
4.4 Neural network architecture details
4.5 Implementation of training process
4.6 Optimization techniques employed
4.7 Model evaluation results
4.8 Performance comparison with baseline models

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Implications for future research
5.4 Limitations of the study
5.5 Recommendations for practitioners
5.6 Conclusion

Thesis Overview

This thesis focuses on building a neural network model for time series prediction, aiming to improve upon existing methods and achieve accurate forecasts. The study begins with an introduction to the topic, providing background information, stating the problem, outlining the objectives, limitations, scope, significance, and structure of the thesis. Following the introduction, a comprehensive literature review is conducted, covering traditional methods, neural networks, deep learning, challenges, applications, comparative analysis, recent advancements, and gaps in the literature.

The system design and methodology chapter detail the data collection and preprocessing, feature selection, model architecture, training, optimization, evaluation metrics, hyperparameter tuning, and validation techniques employed in building the neural network model. The system implementation chapter provides a thorough description of the dataset, preprocessing steps, feature engineering process, neural network architecture, training, optimization, model evaluation results, and performance comparison with baseline models.

The conclusion and summary chapter recapitulates the findings, highlights contributions to the field, discusses implications for future research, acknowledges study limitations, offers recommendations for practitioners, and concludes the thesis. The thesis aims to contribute valuable insights to the field of time series prediction using neural networks and serves as a foundation for further research in this area.

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