This repository is dedicated to a comprehensive time-series analysis of influenza trends within the United States.

The framework adopted is a multifaceted approach that consists of the following key components:

1. Pattern Recognition: Initial identification of recurring patterns, seasonality, and anomalies within the historical flu data.

2. Model Estimation: Formulation and calibration of a mathematical model that encapsulates the underlying mechanics driving flu trends. This includes the use of classical time-series models like ARIMA.

3. Statistical Validation: Rigorous evaluation of the constructed model using advanced statistical tests, ensuring its robustness and reliability for predictive analysis.

4. Predictive Analytics: Utilizing the validated model to forecast upcoming trends in flu activity.

5. Outlier Management: Identification and treatment of anomalous data points that may skew the analysis, thereby ensuring the integrity and accuracy of the study.

6. Report Generation: A comprehensive report is encapsulated in the report, which provides in-depth insights, visualizations, and the statistical rationale behind the model’s configuration and performance.