Self-Supervised Extraction of Human Factors Improves Time Series Forecasting

Global: Self-Supervised Extraction of Human Factors Improves Time Series Forecasting

A new self-supervised learning framework called HINTS has been introduced to improve financial and economic time series forecasting by extracting latent human factors directly from residuals, eliminating the need for external data sources. The research, posted on arXiv in December 2025, aims to reduce the financial, computational, and practical costs associated with integrating news or social‑media streams into predictive models.

Methodology Overview

HINTS incorporates the Friedkin‑Johnsen opinion dynamics model as a structural inductive bias, allowing the system to represent evolving social influence, memory, and bias patterns within the data itself. By treating residuals as a proxy for unobserved human behavior, the framework learns representations that capture collective psychology without explicit external inputs.

Integration with Forecasting Models

The extracted latent factors are transformed into an attention map that is injected into a state‑of‑the‑art forecasting backbone. This integration enables the backbone to weigh time steps according to the inferred human influence, thereby refining its predictive capacity while preserving the original model architecture.

Empirical Evaluation

Experimental results reported across nine real‑world and benchmark datasets demonstrate that HINTS consistently enhances forecasting accuracy compared with baseline models that lack the human‑factor component. Performance gains are observed across diverse domains, including stock indices, commodity prices, and macro‑economic indicators.

Interpretability and Case Studies

Multiple case studies and ablation analyses validate the interpretability of the extracted factors. Researchers found strong semantic alignment between the attention patterns produced by HINTS and documented real‑world events such as policy announcements and market‑moving news, suggesting that the framework captures meaningful human-driven signals.

Implications and Future Work

The study highlights a viable path toward reducing reliance on costly external data streams while still accounting for human dynamics in economic forecasting. Future research may explore extending the opinion‑dynamics bias to other domains, such as epidemiology or climate modeling, where collective behavior plays a pivotal role.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.