Ai causal models and large language model forecasting

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Reference no: EM133700761

Programming AI for Business Analytics

Assessment - AI Causal Models and Large Language Model Forecasting Project

Assessment - Practical Individual Project

Your Task

You are required to:
Create a "bi-annual report" for the Australian "Directorate for Geopolitical and Economics Analytics".
Showcases your creative, analytical, and technical strengths in the application of AI-causal models with Large Language Models for forecasting.

Background
Australia has established the "Directorate for Geopolitical and Economics Analytics" (DGEA) and you have been appointed as the Director for Business Analytics.

Your core responsibilities are:
To apply AI Causal and Large Language Models for the analyse macroeconomic data.
To create the bi-annual "Geopolitical and Economics Outcomes" report for the Directorate that details the causal factors responsible for the shifts in Australian macroeconomic indicators such as inflation rate, interest rates, and overall economic growth.
To provide a short term, 3-6 months forecasts, using the latest advancements in AI, specifically, that of NeuralProphet and LLMs.
To identify macroeconomic causal factors that have an impact on Australian inflation rate and overall economic growth.
To make strategic recommendations on the Monetary and Economic Policy to the Australia Government.

The DGEA Global Macroeconomics Dataset
The Directorate for Geopolitical and Economics Analytics harvests and analyses a large macroeconomic dataset that includes data for the following economies:
Australia, US, EU, and BRICS
And includes the following set of economic indicators:
GNI - Gross National Income
Core and Headline Inflation rates

Interest rate
GDP (Power Purchasing Parity)
Currency exchange rates
Broad Money Supply
The central bank cash rate
Unemployment Rates
Debt-to-GDP Ratio

Assessment Instructions

Section #1: Forecast the Inflation Rate and Economic Growth without Causal Factors
For 3 - 6 months period into the future, forecast the following using NeuralProphet:

Australia's inflation rate, and
Australia's GDP.

Section #2: Correlation and Identification of Causal Factors

Use XGBoost with SHAP to determine the correlation of predictors with the CPI or Inflation Rate as the outcome.
Use EconML to identify which of the correlation factors are causal.
Generate a table of p-values.
Visualise the corresponding ATE Chart.

Section #3: Forecasting with Causal Factors
For 3 - 6 months period into the future, forecast the Australian Inflation Rate (CPI) using the causal factors as external regressors:
Forecast each of the causal factors using Large Language Model or Generative AI.
Use the forecasted values for each of causal factors as external predictor variables and
forecast the Australian CPI using NeuralProphet.
Export the forecasts as CSV and visualise using Exploratory.io or PowerBI or Tableau.

Section #4: Create a Bi-annual Directorate Report
Create the Directorate Bi-Annual Report using the insights from Sections 1 - 3. Your report must contain the following:
An artistic Cover Page
You may use Canva to generate the Cover Page.
You may also use LLMs to generate the artwork.
A Contents Page
An Executive Summary (200 words)
An Introduction to the geopolitical economics of Australia, EU, US, and BRICS
Use visuals for storytelling (300 words)
Remember to visualise the Directorate's Global Macroeconomic Dataset
A Methodology Section

Describe and reference LLMs and Causal AI
Results Section (200 words)
Describe the causal factors.
Visualise and describe the forecasts.
Insights and Recommendations Section (300 words)
Identify each insight and write recommendations based on these insights.
During the 3 - 6 months into the future, what will be the challenges faced by Australia?
Your recommendation should include how Australia could strategically position itself within the economies of BRICS, US, and EU.

Reference no: EM133700761

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