Explainable AI Framework for Time Series Prediction in Economic Mental Health Analysis

A 138.9 KB PDF document authored by Ying Yang, last updated on 2026-04-27. It describes a research framework integrating Explainable AI (XAI) techniques directly into time series prediction models for economic mental health analysis. The paper proposes a method combining feature attribution, causal reasoning, and human-centric explanation generation to improve model interpretability while maintaining competitive accuracy.

Use Cases

• Developing interpretable time series forecasting models for mental health indicators based on the described XAI framework.
• Benchmarking intrinsic versus post-hoc explainability techniques in sensitive domains as discussed in the paper.
• Informing policy decisions with AI-driven mental health screening tools that provide human-centric explanations.
• Researching the integration of causal reasoning into predictive models for economic mental health analysis.

Strengths

• The document is openly licensed under CC-BY-4.0, permitting broad reuse.
• File size is precisely 138.9 KB, indicating a concise, focused document.
• The description provides a clear research narrative covering introduction, methods, results, and discussion.

Limitations

• The dataset is a single PDF document (138.9 KB), not a structured data file, limiting direct computational analysis.
• Description metadata is limited; actual data quality and the presence of underlying datasets require manual inspection after download.
• Row count and column-level documentation are absent; any included data tables must be extracted and their semantics inferred.

Provenance

Source

figshare

Collection Method

Likely a research paper or preprint authored by Ying Yang.

Time Range

The document's temporal focus is not specified.

Freshness

Last updated 2026-04-27 13:42:09; freshness should be verified.

Geography

Geographic coverage is not specified.