1 Introduction
2 Problem definition and requirements
2.1 Multi-Period Forecasting
2.2 Challenges in Multi-Period Forecasting
2.3 Functional Requirements
3 Forecasting Techniques
3.1 State Space Models (SSM)
3.2 EWMA (Exponential Weighted Moving Average)
3.3 GARCH (Generalized Auto Regressive Conditional Heteroskedastic Model)
3.4 Specialized Libraries
3.5 Deep Learning Models
3.6 Probabilistic Models
4 Experiments and Results
4.1 Dataset description
4.2 Dataset purpose
4.3 Experimental settings
4.4 Evaluation Metrics
4.5 Benchmark
4.6 Observations
4.7 Benchmark conclusions
5 Conclusion