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Introduction

In recent years, the use of neural networks has gained popularity in various fields, including time series forecasting. Time series forecasting involves predicting future values based on past data, and neural networks have shown promising results in this area. The ability of neural networks to capture complex patterns in data makes them a suitable candidate for building accurate forecasting models.

This thesis aims to explore the use of neural networks for time series forecasting and develop a model that can effectively predict future values. By leveraging the power of neural networks, we hope to improve the accuracy and reliability of forecasting models in various industries.

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 Introduction to time series forecasting
2.2 Neural networks in time series forecasting
2.3 Traditional forecasting methods
2.4 Comparison of neural networks with traditional methods
2.5 Challenges in neural network-based forecasting
2.6 Recent developments in neural network forecasting models
2.7 Applications of neural networks in different industries
2.8 Evaluation metrics for forecasting models
2.9 Advantages and disadvantages of using neural networks for forecasting
2.10 Summary of literature review

Chapter 3: System Design and Methodology
3.1 Introduction
3.2 Data collection and preprocessing
3.3 Feature selection and engineering
3.4 Model selection and architecture
3.5 Hyperparameter tuning
3.6 Training and testing process
3.7 Validation techniques
3.8 Performance evaluation metrics
3.9 Comparison with other forecasting models
3.10 Summary of system design and methodology

Chapter 4: System Implementation
4.1 Introduction
4.2 Implementation of neural network model
4.3 Software and tools used for implementation
4.4 Data visualization and analysis
4.5 Model training and optimization
4.6 Results interpretation
4.7 Model deployment
4.8 Testing and validation
4.9 Performance analysis
4.10 Summary of system implementation

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions of the study
5.3 Implications for future research
5.4 Limitations of the study
5.5 Conclusion
5.6 Recommendations for practitioners
5.7 Recommendations for further research
5.8 Final remarks

Overall, this thesis will provide valuable insights into the application of neural networks for time series forecasting and contribute to the existing body of knowledge in this field. By developing a robust and accurate forecasting model, we aim to assist industries in making informed decisions based on reliable predictions.

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