HINTS: Extraction of Human Insights from Time-Series Without External Sources

TL;DR

HINTS introduces a self-supervised framework that extracts human behavioral insights directly from time series data, enhancing forecasting accuracy without relying on external sources like social media or news.

Contribution

HINTS is the first method to endogenously extract human factors from time series residuals using a structural bias, improving interpretability and accuracy in forecasting models.

Findings

Consistently improves forecasting accuracy across nine datasets.

Extracted factors align semantically with real-world events.

Validated through case and ablation studies.

Abstract

Human decision-making, emotions, and collective psychology are complex factors that shape the temporal dynamics observed in financial and economic systems. Many recent time series forecasting models leverage external sources (e.g., news and social media) to capture human factors, but these approaches incur high data dependency costs in terms of financial, computational, and practical implications. In this study, we propose HINTS, a self-supervised learning framework that extracts these latent factors endogenously from time series residuals without external data. HINTS leverages the Friedkin-Johnsen (FJ) opinion dynamics model as a structural inductive bias to model evolving social influence, memory, and bias patterns. The extracted human factors are integrated into a state-of-the-art backbone model as an attention map. Experimental results using nine real-world and benchmark datasets demonstrate that HINTS consistently improves forecasting accuracy. Furthermore, multiple case studies and ablation studies validate the interpretability of HINTS, demonstrating strong semantic alignment between the extracted factors and real-world events, demonstrating the practical utility of HINTS.