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Introduction

Demand forecasting plays a crucial role in determining optimal pricing strategies for various products and services. In the event ticketing industry, accurate demand forecasting is essential for setting ticket prices that maximize revenue while ensuring good attendance levels. Time series analysis, which involves analyzing historical data to make predictions about future events, has been widely used in demand forecasting for event ticket pricing.

This thesis aims to explore the application of time series analysis in demand forecasting for event ticket pricing using event data. By analyzing historical ticket sales data, event characteristics, and external factors such as seasonality and promotions, we aim to develop a predictive model that can help event organizers set optimal ticket prices.

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 Demand forecasting in the event ticketing industry
2.2 Time series analysis in demand forecasting
2.3 Pricing strategies in event ticketing
2.4 Factors influencing event ticket demand
2.5 Previous studies on demand forecasting for event ticket pricing
2.6 Data sources for demand forecasting
2.7 Machine learning techniques for demand forecasting
2.8 Challenges in demand forecasting for event ticket pricing
2.9 Best practices in demand forecasting for event ticket pricing
2.10 Summary of literature review

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection
3.3 Data preprocessing
3.4 Time series analysis techniques
3.5 Model development
3.6 Model evaluation
3.7 Validation methods
3.8 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Analysis of historical ticket sales data
4.2 Identification of key factors influencing ticket demand
4.3 Development and evaluation of predictive model
4.4 Comparison of different pricing strategies
4.5 Implications for event organizers
4.6 Recommendations for future research

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Conclusions
5.3 Contributions to the field
5.4 Practical implications
5.5 Limitations of the study
5.6 Future research directions

Overall, this thesis will contribute to the existing literature by providing insights into the application of time series analysis in demand forecasting for event ticket pricing. By developing a predictive model based on event data, this research will help event organizers optimize their pricing strategies and improve revenue generation.

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