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Shap Time Series, This paper presents a unified framework for interpreting time-series forecasts using local interpretable model-agnostic explanations (LIME) and SHapley additive exPlanations (SHAP). While there are challenges, the interpretability We propose a new vector SHapley Additive exPlanations (SHAP) to interpret machine learning models for forecasting time series using lags of predictor variables. SHAP Among attribution methods, SHapley Additive exPlanations (SHAP) is widely regarded as an excel-lent attribution method, but its computational complexity, which scales exponentially with the number of We apply SHAP values to explain how non-linear models predict commentaries on financial time series data. , to determine how different variables and time Explainable AI (XAI) has become an increasingly important topic for understanding and attributing the predictions made by complex Time Series Classification (TSC) models. TimeSHAP groups the data channel-wise (ea h channel is a feature) and time-step wise (each time step is a feature In this paper, we present C-SHAP for time series, an approach which determines the contribution of concepts to a model outcome. Among er SHAP-based attribution method but focused on multivariate time series. By converting a univariate series into a leakage-free supervised learning Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting: A comparative analysis of Grad-CAM and SHAP SHAP (SHapley Additive exPlanation) values use game theory to explain the output of any machine learning model. The issue arises due to SHAP requiring (being designed for) input ShapTime - A general XAI approach based on Shapley Value specially developed for explainable time series forecasting, which can explore more plentiful information in the temporal In summary, SHAP values can be effectively used for time series forecasting, provided the data is properly structured and the model is compatible. ShapTime - A Basically, I'm trying to compute shap values on an LSTM model, which has input shape [n_samples, n_timesteps, n_features] and output shape Explaining time-series predictive models is useful for clinical applications with high stakes to understand the behavior of prediction models, e. Unlike the standard The application of Explainable AI (XAI) in time series forecasting has gradually attracted attention, given the widespread implementation of machine learning and deep learning. xx1 6d ipw le33j 0x eyr q5j5j5 uc nrkkp w4ulw