There are five key stages to building a sales forecasting model in MindsDB:
- Data preparation: Connect to sales databases (e.g., MySQL) and ensure that the data contains historical sales, product categories, timestamps, and other characteristic fields
- Model Creation: Execute SQL statement
CREATE PREDICTOR sales_forecast FROM mysql_db.sales_data PREDICT next_quarter_revenue, the system will automatically:- Identify numerical and categorical features
- Identify time series fields (e.g., order_date)
- Configuring the default parameters of the Lightwood framework
- Training Monitoring: By
SELECT * FROM mindsdb.predictors WHERE name='sales_forecast'View training progress and accuracy metrics - Forecast Enquiry: Use a file like
SELECT next_quarter_revenue FROM sales_forecast WHERE product_category='electronics'statements to get the prediction results - Effectiveness Optimization: By
RETRAIN sales_forecast USING ...Adjustment of feature engineering or model parameters
Strengths Statement:
Time savings of approximately 801 TP3T compared to traditional ML processes, and an enterprise case study shows a retailer realizing weekly level sales forecasting accuracy of 921 TP3T.
This answer comes from the articleMindsDB: An Open Source Platform for Connecting Data from Multiple Sources and Querying with SQL and AIThe
May not be reproduced without permission:AI productivity tools " How to build a forecasting model with MindsDB? Sales forecasting as an example to illustrate the key steps
English 
简体中文 
日本語 
Deutsch 
Português do Brasil