This repository is a proof of concept showcasing the integration of a modern data stack, MLOps practices, and AI models for time series forecasting. The goal of this project is to demonstrate end-to-end machine learning workflows, including data ingestion, model training, deployment, and trend visualization.

Disclaimer: This project is for educational purposes only and is not intended for real-world financial trading or investment.

This system consists of multiple components working together to demonstrate AI-driven time series prediction:

• Ingests and preprocesses time series data using modern data stack tools.

• Extracts meaningful features from time series data for model training.

• Implements ML/DL models such as LSTMs, ARIMA, Prophet, and Transformer-based models for forecasting.

• Utilizes CI/CD pipelines for model training, validation, and deployment.

• Tracks model performance and detects data drift over time.

• Generates forecasts and trends from trained models.

• Displays time series predictions and insights using interactive visualizations.

I’m always open to suggestions and improvements! Feel free to reach out or submit a pull request if you’d like to contribute or share your thoughts. 🚀

Would you like to add contact details, GitHub profile links, or documentation references for collaboration? 😊