Predictive Analysis
This feature is an advanced tool designed to perform predictive analysis on financial data within Excel. Using statistical techniques such as linear regression, polynomial regression, and exponential smoothing, it helps forecast future trends based on historical data.
Notes
Data Quality:
The accuracy of predictions heavily depends on the quality and quantity of the historical data provided.
Model Selection
Choose the most appropriate analysis method based on the nature of your data and specific needs.
Validation
It is recommended to compare predictions with real data as they become available to evaluate model accuracy and make necessary adjustments.
How to use
When opening the panel, you will find a list of available analyses:
Predictive Analysis
provides a general overview of future trends.
Linear Regression
analyzes the linear relationship between two variables.
Exponential Smoothing
uses smoothing methods to forecast future values.
Polynomial Regression
fits a polynomial curve to the data to capture more complex relationships.
ARIMA
Multiple Regression
For each method, you will be required to select the appropriate data range in your Excel sheet. Ensure that the data is numerical and free of missing values to obtain accurate results.
Depending on the chosen method, you may need to specify additional parameters, such as the number of periods to forecast or the polynomial degree for polynomial regression.
The function will generate a new worksheet with the analysis results, including:
- Forecasted values for future periods.
- Model performance metrics, such as R-squared and root mean square error (RMSE).
- Charts displaying historical data and future predictions.
Textual interpretations and recommendations based on model performance, helping you better understand trends and make informed decisions.