There is a moment every data professional eventually reaches where forecasting stops feeling like a technical exercise and starts to feel strangely personal. Your models begin to disappoint you in wa...
Updated 2 months ago
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Level Up Your Forecasting with Temporal Fusion Transformers for Data Science in Microsoft Fabric
Amazing breakdown of TFT intuition Sahir_Maharaj
The way you simulated it with `lstm_gate * tempo
ral_vector + attention_gate * attention_vector` makes the whole architecture click. Most people jump straight to PyTorch but this numpy version shows exactly what TFT is doing under the hood - weighting temporal patterns vs attention patterns
That bar plot of attention weights is exactly what makes TFT so powerful for stakeholder trust. "Why did it predict a spike?" - You can literally point to the time steps with highest attention.
I agree 100% on the feature engineering part. Once you see `feature_strength` coming out of the model, you start understanding your data differently.
Going to try this in a Fabric notebook this week with our sales forecasting data. Thanks for making complex models feel approachable!