Collection of Time-Series Classification Datasets with Pretrained Deep Models and SHAP, LIME & Anchor Explanations

A benchmark suite for reproducible time-series XAI research Precomputed boundle of post-hoc explanations and black-box models for time-series classification This dataset contains 83 univariate and 20 multivariate time series datasets from the TSC repository, each used for multiclass classification with a deep learning model. For each dataset, we provide: • Precomputed train/test splits • A trained TensorFlow model • Post-hoc local explanations generated using three methods: SHAP, LIME, and Anchor Impact of the dataset These datasets provide a ready-to-use benchmark for Explainable AI in time series classification. Since models and explanation outputs are precomputed, researchers can immediately use them for evaluation, visualization, or developing new post-hoc XAI techniques. This reduces the overhead of retraining or re-explaining models, supports reproducibility, and enables systematic comparisons across explanation methods. Comprehensive Coverage: 83 univariate and 20 multivariate UCR/UEA time-series, each with a standardized 75/25 train–test split in NumPy .pickle format. Pretrained Models: Ready-to-use ConvLSTM1D TensorFlow models for every dataset, eliminating costly training and ensuring experimental consistency. Precomputed Explanations: Post-hoc outputs for training and test sets from KernelSHAP, LIME, and Anchor, including attribution scores and rule sets with confidence and coverage. Open Data and Code: All files are CC-BY-4.0, and a linked GitHub repository provides Jupyter notebooks for loading data, inspecting outputs, and applying XAI methods. Repository content - `train_test.zip` — contains files of the form `{univariate|multivariate}_{series_name}_train_and_test.zip` Each includes: `trainX.pickle`, `trainy.pickle`, `testX.pickle`, `testy.pickle` Format: `numpy.array` - `models.zip` — trained models as directories in the form `{univariate|multivariate}_{series_name}_model_tf.zip` Each contains a TensorFlow SavedModel. Format: `numpy.array` - `shap.zip` — SHAP values for each dataset in `{series_name}_shap_values.zip` Files: `svtr.pickle` (train), `svts.pickle` (test). Format: `numpy.array` - `lime.zip` — LIME values for each dataset in `{series_name}_lime_values.zip` Files: `lvtr.pickle` (train), `lvts.pickle` (test). Format: `numpy.array` - `anchor.zip` — rule-based Anchor explanations per dataset in `{series_name}_anchor_values.zip` Files: `avtr.pickle` (train), `avts.pickle` (test). Format: `List[List[Dictionary]]`