Forecast Evaluation

This assignment you will use Generative AI to evaluate a number of forecasts you created in Homework 1 and 2.

Google GenAI: This innovative AI platform is a result of a partnership between Rutgers Business School and Google, allowing Business School faculty, staff, and students to access large language models (LLM’s) vendors such as Google, Anthropic, Meta, and many more through a single user interface. The platform brings the power of generative AI to the classroom, allowing users to reference external sites & data sets to provide these modes with contextual information to ground the query results.

Access is available at the URL below using University Sigle-Sign-On.

Generative AI can be useful for interpreting forecasts:

1. Pattern Recognition: Generative AI models, such as those based on deep learning, are adept at recognizing complex patterns within data. This ability is valuable when analyzing historical data and identifying trends or seasonality, which are crucial components of many forecasting models.
2. Handling Multivariate Data: Many forecasting tasks involve multivariate data, where multiple variables affect the forecast. Generative AI models can handle such data effectively, capturing relationships and dependencies among variables to improve forecasting accuracy.
3. Handling Nonlinear Relationships: Generative AI models can model nonlinear relationships between input variables and the target forecast. In some forecasting scenarios, these nonlinear relationships can significantly impact the accuracy of predictions.
4. Learning from Historical Data: Generative AI can analyze extensive historical data to learn from past patterns and behaviors. This is essential for time series forecasting, where the model needs to understand how past observations influence future values.
5. Adaptability: Generative AI models can adapt to changing data patterns over time. This adaptability is crucial in forecasting tasks where the underlying factors affecting the forecast may evolve or experience shifts.
6. Uncertainty Estimation: Some generative AI models can provide estimates of uncertainty, which is essential in forecasting. Knowing the confidence intervals or probability distributions associated with predictions helps decision-makers make informed choices based on the level of risk they are willing to accept.
7. Handling Missing Data: Generative models can handle missing data points gracefully. In real-world forecasting scenarios, data may be incomplete or have gaps, and generative models can provide forecasts even with missing information.
8. Scenario Analysis: Generative AI can simulate various scenarios to assess the potential outcomes of different decisions. This is valuable for risk management and strategic planning, allowing decision-makers to explore the impact of various factors on forecasts.
9. Speed and Automation: Generative AI can generate forecasts quickly and automate the forecasting process, reducing the time and effort required for manual analysis. This is especially beneficial for handling large datasets and frequent updates.
10. Consistency: Generative AI models can maintain consistency in the forecasting process, reducing the potential for human errors and biases that may arise from manual forecasting methods.
11. Scale: Generative AI can handle forecasting tasks at scale, making it suitable for businesses and organizations that need to generate forecasts for numerous products, regions, or time periods simultaneously.
12. Continuous Learning: Some generative AI models support continuous learning, allowing them to adapt and improve their forecasting accuracy as new data becomes available.

This assignment:

1. Pick two of your forecasts from HW 1 and two from HW2. The goal is to compare two of the forecasts using ChatGPT and determine which one is better.
2. First, open the Excel file for the homework and copy and paste the forecast information into your prompt. Then, make sure that you ask for an appropriate analysis of the two forecasts you are comparing and receive appropriate suggestions on what you can do better.
3. Paste your results into a Word document and submit it below.