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Introduction

Time series forecasting has been widely used in various fields such as finance, economics, and business to predict future values based on historical data. In the context of economic indicators, time series forecasting plays a crucial role in analyzing and predicting economic trends, which can help policymakers, investors, and businesses to make informed decisions. This study focuses on the application of time series forecasting techniques to predict economic indicators, with a specific emphasis on understanding the relationships between different economic variables and their impact on forecasting accuracy.

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

Chapter 2: Literature Review
2.1 Overview of Time Series Forecasting
2.2 Economic Indicators and Their Importance
2.3 Traditional Forecasting Methods
2.4 Machine Learning Techniques for Time Series Forecasting
2.5 Accuracy Metrics for Forecasting Models
2.6 Factors Affecting Forecasting Accuracy
2.7 Applications of Time Series Forecasting in Economics
2.8 Challenges and Opportunities in Time Series Forecasting
2.9 Comparative Analysis of Forecasting Models
2.10 Recent Advances in Time Series Forecasting

Chapter 3: Research Methodology
3.1 Data Collection
3.2 Data Preprocessing
3.3 Model Selection
3.4 Model Training
3.5 Model Evaluation
3.6 Parameter Tuning
3.7 Cross-Validation Techniques
3.8 Forecasting Horizon Selection

Chapter 4: Discussion of Findings
4.1 Analysis of Forecasting Results
4.2 Comparison of Different Models
4.3 Interpretation of Forecasting Errors
4.4 Implications for Economic Decision-Making
4.5 Robustness and Sensitivity Analysis
4.6 Future Research Directions

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Implications for Practice
5.4 Limitations of the Study
5.5 Recommendations for Future Research

Overall, this thesis aims to provide a comprehensive overview of time series forecasting for economic indicators, with a focus on the application of advanced techniques to enhance forecasting accuracy. By analyzing the relationships between economic variables and exploring the impact of different factors on forecasting performance, this study contributes to the existing body of knowledge in the field of econometrics and provides valuable insights for policymakers, investors, and businesses.

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