Tutorial: How to Make LSTM Weather Forecasts Explainable Using SHAP and More

A new technical tutorial published on DEV Community guides developers through building an LSTM-based daily temperature forecasting model and then interpreting its predictions. The guide targets practitioners already familiar with Keras who want to add explainability to existing time series models rather than learn neural networks from scratch. It covers essential preprocessing steps, including MinMax scaling and converting temperature data into overlapping 7-day input windows, before training a two-layer stacked LSTM with dropout and early stopping. Three explainability techniques — permutation importance, SHAP, and Integrated Gradients — are then applied to reveal which lag days most influence the model's output. The tutorial highlights that scaling and sequence windowing are critical to stable LSTM training, and that interpretability tools can meaningfully expose how the model weighs recent versus older temperature data.