Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency
Authors: Owen Queen, Tom Hartvigsen, Teddy Koker, Huan He, Theodoros Tsiligkaridis, Marinka Zitnik
NeurIPS 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate TIMEX on eight synthetic and real-world datasets and compare its performance against state-of-the-art interpretability methods. We also conduct case studies using physiological time series. Quantitative evaluations demonstrate that TIMEX achieves the highest or second-highest performance in every metric compared to baselines across all datasets. |
| Researcher Affiliation | Collaboration | Owen Queen Harvard University EMAIL Thomas Hartvigsen University of Virginia, MIT EMAIL Teddy Koker MIT Lincoln Laboratory EMAIL Huan He Harvard University EMAIL Theodoros Tsiligkaridis MIT Lincoln Laboratory EMAIL Marinka Zitnik Harvard University EMAIL |
| Pseudocode | Yes | Algorithm 1: Landmark filtration |
| Open Source Code | Yes | TIMEX is at https://github.com/mims-harvard/Time X |
| Open Datasets | Yes | We employ four datasets from real-world time series classification tasks: ECG [81] ECG arrhythmia detection; PAM [82] human activity recognition; Epilepsy [83] EEG seizure detection; and Boiler [84] mechanical fault detection. |
| Dataset Splits | Yes | We create 5,000 training samples, 1,000 testing samples, and 100 validation samples for each dataset. |
| Hardware Specification | Yes | For computational resources, we use a GPU cluster with various GPUs, ranging from 32GB Tesla V100s GPU to 48GB RTX8000 GPU. |
| Software Dependencies | Yes | We implemented all methods in this study using Python 3.8+ and Py Torch 2.0. |
| Experiment Setup | Yes | Table 6: Training parameters for TIMEX across all ground-truth attribution experiments. Table 7: Training parameters for TIMEX across all real-world datasets used for the occlusion experiments. Table 8: Training parameters for transformer predictors across all ground-truth attribution experiment datasets. Table 9: Training parameters for TIMEX across all real-world datasets used for the occlusion experiments. |