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Introduction:

Time series forecasting for energy prices is a critical area of research that has gained significant attention in recent years. With the increasing demand for energy and the volatility of energy markets, accurate forecasting of energy prices is essential for stakeholders in the energy industry to make informed decisions. This thesis aims to explore various time series forecasting techniques and their application to predict energy prices.

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 Two: Literature Review
2.1 Overview of Time Series Forecasting
2.2 Energy Market and Pricing
2.3 Traditional Forecasting Methods
2.4 Machine Learning Techniques for Forecasting
2.5 Time Series Forecasting for Energy Prices
2.6 Forecasting Accuracy Metrics
2.7 Factors Influencing Energy Prices
2.8 Case Studies on Time Series Forecasting for Energy Prices
2.9 Challenges in Energy Price Forecasting
2.10 Recent Advancements in Forecasting Techniques

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Modeling Techniques
3.5 Evaluation Metrics
3.6 Validation Methods
3.7 Parameter Tuning
3.8 Software and Tools Used

Chapter Four: Discussion of Findings
4.1 Comparison of Forecasting Techniques
4.2 Impact of External Factors on Energy Prices
4.3 Case Studies Analysis
4.4 Limitations of the Models
4.5 Future Research Directions
4.6 Policy Implications
4.7 Recommendations for Stakeholders
4.8 Ethical Considerations

Chapter Five: 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
5.6 Conclusion

Thesis Overview:

Time series forecasting for energy prices is a complex and challenging task due to the dynamic nature of energy markets and the influence of various external factors. This thesis aims to provide a comprehensive analysis of different time series forecasting techniques and their application in predicting energy prices.

The literature review in Chapter Two will provide an overview of time series forecasting, traditional forecasting methods, machine learning techniques, and their application in energy pricing. It will also discuss the factors influencing energy prices, challenges in forecasting, and recent advancements in forecasting techniques.

Chapter Three will outline the research methodology, including the research design, data collection, preprocessing, modeling techniques, evaluation metrics, and validation methods used in the study.

In Chapter Four, the discussion of findings will compare different forecasting techniques, analyze the impact of external factors on energy prices, present case studies, discuss the limitations of the models, and provide recommendations for stakeholders and future research directions.

Chapter Five will present the conclusion and summary of the thesis, highlighting the key findings, contributions to the field, implications for practice, limitations of the study, recommendations for future research, and a final conclusion.

Overall, this thesis aims to contribute to the body of knowledge on time series forecasting for energy prices and provide valuable insights for stakeholders in the energy industry.

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