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

A PDF document authored by Ying Yang, last updated on April 27, 2026, proposes a novel framework integrating Explainable AI (XAI) into time series prediction for economic mental health analysis. The 1.1 MB file describes a method combining intrinsic and post-hoc interpretability techniques to improve model transparency while maintaining competitive accuracy. The framework is intended to support decision-making for policymakers and mental health professionals.

Use Cases

• Developing transparent time series forecasting models for mental health indicators based on the described XAI framework.
• Benchmarking interpretability techniques like feature attribution and causal reasoning in predictive analytics as discussed in the document.
• Informing the design of AI-driven mental health screening tools that require high trust and domain alignment, as proposed in the methods.

Strengths

• Document is licensed under CC-BY-4.0, permitting broad reuse and adaptation.
• File size is 1.1 MB, ensuring quick download and accessibility.
• Last update timestamp is precisely recorded as 2026-04-27 13:42:09.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is a PDF document, which may require text extraction for computational analysis.

Provenance

Source

figshare

Collection Method

Proposed framework described in an academic-style PDF.

Time Range

Publication date implied by last update in 2026.

Freshness

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

Geography

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