1 Introduction  
1.1 The Contributions of this paper  
1.2 Organization  

2 Rare event data  
2.1 Datasets with rare events – Analysis of existing datasets with rare events  

3 Data Processing Approaches  
3.1 Objectives of Data Processing Approaches  
3.2 Data Cleaning (DC)  
3.3 Feature Selection (FS)  
3.4 Sampling (SL)  
3.5 Feature Engineering (FE)  

4 Algorithmic Approaches  
4.1 Significance of algorithmic approaches  
4.2 Supervised Classification and Regression Methods  
4.3 Clustering Methods  
4.4 Statistical Modeling  
4.5 Meta-Heuristic Optimization  
4.6 Advanced Learning Methods  
4.7 Comparison of algorithmic approaches  

5 Evaluation Approaches  
5.1 Significance of evaluation  
5.2 Evaluation Methodologies  
5.3 Performance metrics  
I) Downstream tasks: Classification and Forecasting  
II) Downstream task: Clustering  
II) Downstream task: Regression  
III) Downstream task: Simulation  

6 Research Findings and Discussion  
6.1 The gaps in current literature  
6.2 Open challenges of rare event prediction  
6.3 Research trends in rare event prediction  

7 Vision Forward  

8 Conclusion