Episode 75 of The Data Science Podcast dives into Temporal Fusion Transformers (TFT), a deep learning architecture that's changing how data scientists handle multi-horizon time series forecasting. Lucas and Luna break down how TFT combines interpretable components like variable selection networks with attention mechanisms to produce accurate, explainable forecasts. They walk through a real-world case: a mid-sized retailer using TFT to predict daily store-level demand across 500 stock-keeping units, handling seasonality, promotions, and exogenous variables like weather. The hosts explain why TFT outperforms traditional ARIMA and gradient boosting on datasets with multiple time series and static covariates, and they discuss the trade-offs in training complexity. TFT is especially powerful when you need both high accuracy and regulatory interpretability — think supply chain planning or energy load forecasting. Lucas and Luna contrast it with simpler models and highlight open-source implementations from PyTorch Forecasting and Google's TensorFlow Probability. The episode also touches on when not to use TFT: small datasets or when interpretability isn't a priority. By the end, listeners understand the architecture's key components and when to reach for TFT in their own projects.

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