Financial Time Series Prediction using Deep Learning

MUST have deep expertise in Large Language Model, GPT, NLP, and Time Series analysis, PyTorch This project is time sensitive, I need it ASAP. DEADLINE IS Aug 6, at 5PM EST. I would like to know how the Transformers diviner model provided below from Github can be used to forecast various time series in the original scale, and make sense of them. The model is already coded, trained, and tested, therefore the original coding should not be changed. Rather you are just adding extra coding at the bottom for the different kinds of dataset. Disregard all the time series provided in the original coding like ETT, WTH, etc, and from that rather just focus on the Exchange/gold prices data 60 day gold prices, and the new data that I recommend. These is a sequential project, meaning you should only move to the next point once you fully complete the current point. Use good visuals 1. Forecasting Gold Prices: - After obtaining the MSE/RMSE (already provided in the code), provide code to display all test sample predictions in the original scale alongside the actual prices, and the dates from the original table. (COMPLETE THE FULL REQUIREMENT HERE, BEFORE MOVING TO THE NEXT POINT) - Create a table with columns for date, "true" prices, and "pred" prices for a 60-day period for the entire test sample. For the predictions, I want to see each prediction in the time step until the 60 day period. Generate graphs in the original scale for gold prices and for the other asset prices to visualize "true" vs. "pred" performance over time. . For example, the goal is a table for Gold: Date, True, Pred of each time steps as column headers, underneath it, have the exact date the test sample, have the original price of the specific date, and have the forecasted values for each time step until the 60th day. So I would know, e.g Jul 19th 2018, the actual price was 1551,.. 60th day, the forecast from the model was 1544.25. So I know exactly how the model performed in real time. I know that if 60 days from today say in Sept 29, the model is telling me the price of Gold will be 1510, I want to see how that model actually performed when we actually get to that specific date. In a table and graph, that is what the project was about. 2. Data Collection and Forecasting for Multiple Assets: - Show how you’re pulling data from Excel file (csv) that contains the date, TARGET FEATURE, and THE VARIABLES NEXT TO IT, and HOW YOU are UPLOADing THAT INTO THE MODEL AND THE MODEL FORECASTED BASED ON ALL THOSE INFORMATION. - Daily Apple stock prices excel (alognside its P/E ratio and Earnings per Share for a 10 year period). 3. Attention Weights Analysis: - Clearly code and Display attention weights for the features/variables used to rank by importance in percentage terms. Use both the traditional attention weight for all layers, and separately the InverseDeepthDifferenceBlock because the original authors used it for a reason. (Clearly show/make notes how you are interpreting that visual. They should have dark backgrounds.) The point of the attention weight part is to know for instance since the model is forecasting Apple stock GDP to be 150, in the next 60 days, the from the variabes/features used in the model, attention weight is telling me that P/E ratio was responsible for 40% of that forecast, EPS was response for 20% of that forecast based on the attention weights results. 4. Steps 1 to 3 for new data: EUR/USD spot price 10 years of data as target feature along with two variables, U.S. interest rates and U.S. trade balance (net trade deficit for a 10 year period). 5. Repeat steps 1 to 3 for 10 years of data for US Real GDP as target feature along with three variables, U.S. interest rates and U.S. inflation CPI, and China’s Real GDP 6. Use the model to forecast for 60 days each of these 4 target features simultaneously, just like in the original mode it simultaneously forecasts ETT, WTH, etc 7. Implementation Details: - Ensure coding compatibility with Google Colab, utilizing an A100 GPU, because that’s what I’ll use 8. 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: - 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: - Transformers Diviner Model: [Diviner-Nonstationary-time-series-forecasting]([login to view URL]) Please let me know if anything is unclear and how soon can I expect the project. THIS PROJECT IS TIME SENSITIVE