ArticleApplied intelligence (Dordrecht, Netherlands)2022
Evaluation of interpretability methods for multivariate time series forecasting.
Article in Applied intelligence (Dordrecht, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- FaXNet: a frequency-adaptive, explainable, and uncertainty-aware network for influenza forecasting.Frontiers in public health · 2026Article
- Transformer model to determine spatio-temporal relationships of variables, and interpretability for soybean seed yield, oil, and protein prediction.Frontiers in artificial intelligence · 2026Article
- An explainable covariate compartmental model for predicting the spatio-temporal patterns of dengue in Sri Lanka.PLoS computational biology · 2025Article
- Prediction of future customer needs using machine learning across multiple product categories.PloS one · 2024Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Being able to interpret a model's predictions is a crucial task in many machine learning applications. Specifically, local interpretability is important in determining why a model makes particular predictions. Despite the recent focus on interpretable Artificial Intelligence (AI), there have been few studies on local interpretability methods for time series forecasting, while existing approaches mainly focus on time series classification tasks. In this study, we propose two novel evaluation metrics for time series forecasting: Area Over the Perturbation Curve for Regression and Ablation Percentage Threshold. These two metrics can measure the local fidelity of local explanation methods. We extend the theoretical foundation to collect experimental results on four popular datasets. Both metrics enable a comprehensive comparison of numerous local explanation methods, and an intuitive approach to interpret model predictions. Lastly, we provide heuristical reasoning for this analysis through an extensive numerical study.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.