Dynamic Network in Time Series

YOU MUST have deep expertise in GRU, GIN, RNN, GNN, Time Series analysis. This project is time sensitive, I need it ASAP.

I would like to know how the Dynamic Network model provided below from Github can be used to forecast/detect critical transition points in time series in the original scale of the data, and make sense of them. Critical transition in this case could be the change of trend/turning point BEFORE WE GET TO THAT POINT. It could be the percentage level, price point or date.

These is a sequential project, meaning you should only move to the next point once you fully complete the current point. Use good visuals

The model is already coded, trained, and tested, therefore the original coding should not be changed. Rather, you will understand its inner workings, architecture, structure and replace the savannah rainfall data and tailor the model to detect the critical transitions points in 10 years of gold prices (from 2012-2022) , then try on 10 yrs of US Real GDP data (2012-2022), 10 years of APPL stock prices, and 10 yrs of EUR/USD spot prices. You’ll are just adding extra coding at the bottom for the different kinds of dataset to align with the methodology of the original code. Disregard all the time series provided in the original coding like savannah rainfall, etc

Data Collection and Forecasting for Multiple Assets: - Show how to pull data from an Excel file (the file should have date column, target feature, and variables next to each other for 10 years) for: - Daily Apple stock prices (alongside its variables being P/E ratio and Earnings per Share for a 10 year period). - Daily EUR/USD spot prices (alongside its variables being U.S. interest rates and U.S. trade balance (net trade deficit) for a 10 year period). - U.S. GDP Real numbers (alongside its variables like U.S interest rates and China’s GDP number for 10 years). You can source the data from free and reliable platforms like Yahoo Finance, World Bank, or FRED St Louis Federal reserve website. I won't be providing any dataset to you. The data and process should be structured for illustration purposes because later I’ll replicate it with different datasets using Excel to feed into the model. - Generate tables and graphs in the original scale to clearly show from each trend/s where the critical transition point happened and how it could have been identified beforehand for the prices for each asset or GDP Clearly make notes where and how you are interpreting the code and the output

5. Implementation Details: - Ensure coding compatibility with Google Colab, using A100 GPU, because that’s what I’ll use.
- Include clear notes in the code for ease of understanding and replication, because eventually I’ll be using different assets with different kinds of variables.

. 6. Project Timeline: - I’m assuming this project is straight forward because most of the hard coding is already there. This project is time sensitive, I need it ASAP GitHub Links: - Dynamic Network Model: [Tipping Predictor](https://github.com/m-serious/tipping-predictor/tree/main) Please let me know if anything is unclear and how soon can I expect the project. THIS PROJECT IS TIME SENSITIVE. MAXIMUM TIME LIMIT FOR THIS PROJECT IS 10 HOURS. I NEED IT BY FRIDAY, AUG 2 AT 12 PM EST