Easy ML Forecasting in Microsoft Fabric - Prophet Framework Tutorial

I see Finance teams spending days and weeks building Excel forecasts that break the moment business patterns shift. There's a better way. I just published a walkthrough showing how to implement ML-based forecasting in Microsoft Fabric - achieving >95% accuracy in hours rather than days/weeks.

Once configured in Fabric notebooks, forecasts refresh automatically. No more monthly Excel gymnastics. CFOs get conservative/baseline/stretch scenarios from the same model. And it adapts to trend changes without manual recalibration.

The approach works beyond AR (Accounts Receivable) - I've used similar frameworks for sales forecasting, inventory planning, and capacity projections across Telco, Oil & Gas, and Pharma clients.

What the tutorial covers:

Prophet framework for automatic seasonality detection
12-month cash flow predictions with confidence intervals for scenario planning
Lakehouse integration for automatic Power BI refresh
Cross-validation workflow that tunes parameters automatically

Real accuracy metrics: With my sample data I was able to achieve 3% MAPE (Mean Absolute Percentage Error) - that's $50K average variance on $1.5M monthly collections. Industry target is under 5%.

The approach works beyond AR (Accounts Receivable) - I've used similar frameworks for sales forecasting, inventory planning, and capacity projections across Telco, Oil & Gas, and Pharma clients.

What the tutorial covers:

Prophet framework for automatic seasonality detection
12-month cash flow predictions with confidence intervals for scenario planning
Lakehouse integration for automatic Power BI refresh
Cross-validation workflow that tunes parameters automatically

Real accuracy metrics: With my sample data I was able to achieve 3% MAPE (Mean Absolute Percentage Error) - that's $50K average variance on $1.5M monthly collections. Industry target is under 5%.