We provide a codebase for TimeInf: Time Series Data Contribution via Influence Functions. TimeInf is a data contribution estimation method for time-series datasets. TimeInf uses influence functions to attribute model predictions to individual time points while preserving temporal structures. Our extensive empirical results demonstrate that TimeInf outperforms state-of-the-art methods in identifying harmful anomalies and helpful time points for forecasting. Additionally, TimeInf offers intuitive and interpretable attributions of data values, allowing us to easily distinguish diverse anomaly patterns through visualizations.
conda create --name timeinf
conda activate timeinf
git clone https://github.com/yzhang511/TimeInf.git
cd TimeInf
pip install -e .
Datasets for time series anomaly detection are available here. Please cite the original sources of the datasets.
Example scripts using TimeInf and other baselines for anomaly detection: anomaly_detection/main_SMAP_MSL.py.
Example notebooks for the application of linear, black-box, and non-parametric TimeInf: demo.