📋 Store 36 – Forecasting Accuracy Summary

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Time Series Forecast - Store 36 ML Forecast - Store 36

📋 Store 36 – Forecasting Accuracy Summary

Model MAE ($) MAPE (%) RMSE ($)
Excel 5,054 45.37% 5,658
Machine Learning 1,166 9.99% 1,431

At Store 36, machine learning outperformed Prophet by a wide margin — reducing forecasting error by over 70% and giving the team a clearer picture of what's coming.

We evaluate forecast performance using three key metrics:

  • MAE (Mean Absolute Error): This tells us how far off the forecast was each day, on average — in dollars. Lower is better.
  • MAPE (Mean Absolute % Error): A percentage-based measure that shows how inaccurate the forecast was, relative to actual sales.
  • RMSE (Root Mean Squared Error): This emphasizes the big forecasting mistakes. It’s more sensitive to major sales swings — and a strong indicator of risk.

Traditional Excel-style forecasts tend to miss large spikes and drops, especially during volatility. Our machine learning model adapts to changing trends, promotions, and seasonality — giving you forecasts you can actually rely on.

Bottom line: lower forecast error means better planning, less waste, and fewer surprises.

💡 Why Accurate Forecasting Matters

  • 📉 Protect Cash Flow: Poor forecasts lead to wasted inventory, missed revenue, and bad staffing decisions.
  • 📅 Plan with Confidence: Reliable forecasts improve budgeting, purchasing, and sales alignment.
  • ⚠️ Spot Trouble Early: ML models detect downturns sooner — giving you time to act, not just react.

🚀 Want to See It with Your Data?

If your business has a few months or years of daily or weekly sales, we can:

  • ✅ Review your data for free
  • ✅ Deliver a sample forecast
  • ✅ Help you see if ML forecasting is a good fit

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