{"id":18580,"name":"Forecast Explainable","purpose":"A Keras plugin that integrates explainability methods (permutation importance, SHAP, Integrated Gradients) directly into LSTM models for time series forecasting, making the prediction process transparent and understandable to users.","profitable":1,"date_generated":"Saturday July 2026 09:09","reference":"forecast-explainable","technology_advise":["Python","Medium"],"development_time_estimation_mvp_in_hours":100,"grade":7.0,"category":"devtools","view_count":10,"similar_ideas":[{"id":19739,"name":"Model Explainer Insights","grade":7.2,"category":"devtools"},{"id":17734,"name":"Explainable AI Insight Engine","grade":7.8,"category":"ai"},{"id":3894,"name":"Counterfactual Explanation API","grade":7.8,"category":null},{"id":16128,"name":"Subquadratic AI Explainability Platform","grade":7.8,"category":"ai"},{"id":16170,"name":"Loss Function Insights Platform","grade":7.8,"category":"devtools"}],"source_headline":"LSTM architectures can be annoying to trust for weather prediction"}