Probabilistic-Rainfall-Analysis_NYC

This paper analyzes 140 years of Central Park precipitation using methods like SARIMAX, Marked Point Processes, and LSTM networks. Gaussian processes include frequency analysis, predictive modeling, and uncertainty quantification to explore precipitation trends and improve forecasting accuracy.

Key Features:

• Frequency Domain Analysis to assess periodicity.
• SARIMAX Models for trend and seasonal predictions.
• Marked Point Process (MPP) for impulse-like behavior analysis.
• LSTM Neural Networks for temporal correlation modeling.
• Gaussian Process for uncertainty quantification of residuals.

Authors:

• Omid Emamjomehzadeh (Conceptualization, LSTM and GP modeling, report writing)
• Ahmadreza Ahmadjou (SARIMAX modeling, MPP analysis, report contributions)
• Ruixuan Zhang (Frequency domain analysis, visualizations, report contributions)

Dataset:

The dataset is publicly available on Kaggle: Central Park Weather Data 1869-2022.

Citation:

If you use this repository, please cite the associated report and GitHub repository.

Clone the Repository:

git clone [https://github.com/omidemam/Probabilistic-Rainfall-Analysis_NYC.git]