cuddles47 / streamflow-forecasting

Streamflow Forecasting with ARIMA and VAR

Streamflow Forecasting with ARIMA and VAR 🌊🔮

Overview

This repository hosts a comprehensive streamflow forecasting project that employs two powerful time series forecasting techniques: ARIMA (AutoRegressive Integrated Moving Average) and VAR (Vector Autoregression). Streamflow forecasting is critical for effective water resource management, and this project aims to provide accurate predictions using advanced statistical methods.

Key Features 🌟

ARIMA Modeling

• Implementation of ARIMA models to capture temporal patterns and autocorrelation in streamflow data.
• Fine-tuning model parameters for optimal forecasting performance.

VAR Modeling

• Application of Vector Autoregression models to account for potential interdependencies among multiple time series variables affecting streamflow.
• Exploration of relationships and interactions among various hydrological factors for more nuanced predictions.

Data Preparation and Exploration 🛠️🗺️

• Preprocessing of streamflow data to handle missing values, outliers, and ensure the quality of input data for the models.
• Exploratory data analysis (EDA) to gain insights into the underlying patterns and characteristics of the streamflow time series.

Evaluation and Validation 📊✅

• Rigorous evaluation metrics to assess the accuracy and reliability of the forecasting models.
• Cross-validation techniques to validate the models' generalization performance on diverse datasets.

Repository Structure 📂

• ARIMA_Modeling.ipynb: Jupyter Notebook with the implementation and analysis of ARIMA models.

• VAR_Modeling.ipynb: Jupyter Notebook detailing the application of Vector Autoregression models for streamflow forecasting.

• Data_Preparation.ipynb: Notebook focusing on data preprocessing steps, handling missing values, and exploratory data analysis.

• Evaluation_and_Validation.ipynb: Notebook dedicated to model evaluation, validation, and comparison of ARIMA and VAR performance.

• data/: Directory to store raw and processed streamflow datasets.

• results/: Directory to save model outputs, forecasts, and evaluation results.

Getting Started 🚀

1. Clone the repository:

   git clone https://github.com/your-username/streamflow-forecasting.git

well it seem to be unable to use tensorflow ,speech recognition and some other python lib in Python 3.13.0a2 so i would recommend downgrade the python version , highly recoomend python 3.9.13 which i am currently using for it performence and stablity