Update README.md

README.md

@@ -12,17 +12,20 @@ short_description: DATFID - Secure and powerful forecasting SDK
 
 **DATFID** delivers reliable and interpretable forecasting for any time series. Our models consistently rank at the top—see our website [datfid.com](https://datfid.com) for live results.
 
-We achieved **state-of-the-art accuracy in the M5 Forecasting Competition**, beated all the benchmarks, forecasting hierarchical Walmart product sales over a 5-year period using advanced exogenous features and rigorous error metrics ([christophenicault.com](https://www.christophenicault.com/post/m5_forecasting_accuracy/?utm_source=chatgpt.com)).
+We achieved **state-of-the-art accuracy in the M5 Forecasting Competition**, beated all the benchmarks, forecasting hierarchical Walmart product sales over a 5-year period using advanced exogenous features and rigorous error metrics ([Kaggle M5 page](https://www.kaggle.com/c/m5-forecasting-accuracy)).
 
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 ##  Why DATFID Stands Out
 
+- **Professionally recognized benchmarking**  
+  M5 is part of the renowned “M-Competitions” series, used worldwide to compare forecasting methods under realistic conditions. ([Makridakis Competitions, Wikipedia](https://en.wikipedia.org/wiki/Makridakis_Competitions)) :contentReference[oaicite:12]{index=12}.
+
 - **Hierarchical & intermittent series**  
-  DATFID handles complex retail data with product-store hierarchies, zero-heavy series, pricing, calendar events, and promotional features—all core challenges of the M5 setup ([cienciadedatos.net](https://cienciadedatos.net/documentos/py61-m5-forecasting-competition?utm_source=chatgpt.com)).
+  DATFID handles complex retail data with product-store hierarchies, zero-heavy series, pricing, calendar events, and promotional features—all core challenges of the M5 setup ([Background & Organization of the M5 Competition](https://www.sciencedirect.com/science/article/pii/S0169207021001187)) :contentReference[oaicite:13]{index=13}.
 
 - **Robust accuracy**  
-  Scoring well on 42,840 series across multiple aggregation levels is a testament to our method’s strength—especially on challenging intermittent product-level data ([christophenicault.com](https://www.christophenicault.com/post/m5_forecasting_accuracy/?utm_source=chatgpt.com)).
+  Scoring well on 42,840 series across multiple aggregation levels is a testament to our method’s strength—especially on challenging intermittent product-level data.
 
 - **Fast, scalable, and interpretable**  
   Unlike “black-box” approaches, we prioritize transparency and efficiency without sacrificing accuracy.