Decoding Human Influence in Time-Series Data

Author: Denis Avetisyan

A new framework reveals hidden human-driven patterns within time-series, enhancing forecasting and providing deeper insights into underlying dynamics.

Human-centric forecasting reframes conventional signal-centric modeling by explicitly extracting latent human influence directly from raw data - inspired by opinion dynamics - and integrating these factors into established forecasting models, offering a structured, interpretable pathway from data to informed decisions without reliance on external information sources. — Human-centric forecasting reframes conventional signal-centric modeling by explicitly extracting latent human influence directly from raw data – inspired by opinion dynamics – and integrating these factors into established forecasting models, offering a structured, interpretable pathway from data to informed decisions without reliance on external information sources.

This paper introduces HINTS, a self-supervised approach that extracts human factors from time-series residuals using residual analysis and attention mechanisms, improving accuracy without external data.

While financial and economic time series are demonstrably shaped by human behavior, many forecasting models rely on costly and complex external data to capture these influences. This study introduces HINTS: Extraction of Human Insights from Time-Series Without External Sources, a novel self-supervised framework that distills latent human-driven dynamics directly from time-series residuals using principles of opinion dynamics. By leveraging the Friedkin-Johnsen model and an attention mechanism, HINTS consistently improves forecasting accuracy and offers interpretable insights into the underlying factors driving temporal patterns. Could this approach unlock a deeper understanding of market psychology and improve predictive modeling across diverse domains?

Decoding the Signals of Collective Behavior

Many conventional time-series forecasting models prioritize identifying and extrapolating established patterns – trends and seasonality – but frequently neglect the pervasive, yet often subtle, impact of human agency. Data reflecting collective activities, such as retail sales, website traffic, or even energy consumption, isn’t simply a product of predictable forces; it’s also shaped by complex psychological factors, social interactions, and even spontaneous events. These behavioral influences introduce non-linearities and irregularities that traditional statistical methods, designed for smoother, more predictable data, struggle to capture effectively. Consequently, forecasts can be systematically biased or miss critical turning points, as they fail to account for the inherent unpredictability stemming from the human element within the data itself. Recognizing this embedded behavioral influence is crucial for improving the accuracy and relevance of time-series analysis across diverse fields.

Conventional time-series forecasting techniques, such as DLinear, excel at identifying and extrapolating consistent, linear progressions within data. However, these methods frequently fall short when confronted with the complexities of human-influenced systems. Collective human behavior introduces inherent non-linearities and, at times, seemingly irrational fluctuations that defy simple linear prediction. Phenomena like sudden shifts in consumer sentiment, unpredictable viral trends, or the cascading effects of social influence create patterns that are poorly captured by models designed for stable, predictable processes. Consequently, relying solely on linear extrapolation in these contexts can lead to significant forecasting errors, as the underlying dynamics are governed by factors beyond consistent trends and seasonal variations.

The seemingly random fluctuations remaining after accounting for predictable patterns in time-series data – the residual component – often conceal a wealth of information about underlying human influences. Conventional forecasting prioritizes capturing overarching trends and seasonal cycles, effectively smoothing over the subtle, yet significant, deviations caused by collective behavior. However, this residual noise isn’t simply error; it represents the aggregate effect of individual decisions, reactions, and adaptations. Analyzing these residuals, through techniques beyond traditional statistical modeling, can reveal previously hidden dynamics – offering insights into how human actions shape complex systems, from financial markets to social networks. This approach allows researchers to move beyond predicting what will happen, towards understanding why it happens, recognizing that even apparent randomness can be a signature of human agency.

HINTS enhances forecasting by first extracting human-influenced signals <span class="katex-eq" data-katex-display="false"> \mathcal{H}_{D}^{T} </span> from time series data <span class="katex-eq" data-katex-display="false"> \mathcal{X}_{D}^{T} </span> and then using these signals to modulate a forecasting model, resulting in predictions <span class="katex-eq" data-katex-display="false"> \mathcal{Y}_{D}^{T} </span>. — HINTS enhances forecasting by first extracting human-influenced signals $\mathcal{H}_{D}^{T}$ from time series data $\mathcal{X}_{D}^{T}$ and then using these signals to modulate a forecasting model, resulting in predictions $\mathcal{Y}_{D}^{T}$ .

HINTS: A Framework for Unveiling the Human Factor

The HINTS framework initiates its analysis with a time series decomposition process. This stage separates the observed time series into constituent components – typically trend, seasonality, and a residual. The residual component, representing the variance not explained by systematic patterns, is then isolated for further analysis. This decomposition is a prerequisite to the self-supervised learning stage, as it focuses the subsequent learning process on the signal potentially attributable to the Human Factor, effectively removing predictable variations and noise from the data before feature extraction. This two-stage approach allows HINTS to concentrate on subtle, non-systematic changes within the time series.

HINTS utilizes self-supervised learning to generate latent representations of the Human Factor from the residual component of time series data. This approach contrasts with traditional statistical modeling which typically relies on explicitly defined features or assumptions about the underlying data distribution. By learning directly from the residuals – the unexplained variance after decomposition – the framework identifies patterns indicative of human influence without requiring labeled data or predefined characteristics. The resulting latent representations capture complex, non-linear relationships within the time series, enabling the model to discern human-driven variations that might be missed by purely statistical methods. This allows for a more nuanced understanding of the Human Factor and its contribution to observed time series behavior.

The HINTS framework distinguishes itself by operating solely on the data contained within the observed time series, eliminating the need for supplementary external datasets. This reliance on intrinsic signals-the inherent patterns and variations present in the time series itself-reduces dependency on potentially biased or unavailable external information. By extracting the Human Factor exclusively from these internal dynamics, HINTS offers a self-contained solution, enhancing its adaptability and robustness across diverse datasets and application scenarios where external data may be limited or unreliable.

Sensitivity analysis reveals that the Human Factor weighting parameter γ in Stage 2 of HINTS significantly influences overall system behavior.

Modeling Human Dynamics Through Opinion Dynamics

The Friedkin-Johnsen model is utilized as a structural inductive bias to constrain the interpretation of observed human behavioral patterns, effectively guiding the analysis towards plausible explanations rooted in established social science theory. This involves representing individual belief updates as a weighted combination of influences from other agents and the individual’s own prior belief. Mathematically, the model is defined as $x_i(t+1) = \mu x_i(t) + \sum_{j \in N} w_{ij} (x_j(t) - x_i(t))$ , where $x_i$ represents the opinion of agent i at time t, μ is a parameter representing the strength of individual memory, $N$ denotes the set of agents influencing i, and $w_{ij}$ represents the weight of influence from agent j to agent i. By framing the analysis within this established model, the system prioritizes interpretations consistent with known principles of social influence and belief formation, reducing the risk of spurious correlations or overfitting to noise in the data.

The Friedkin-Johnsen model posits that individual belief updates are a weighted combination of two primary factors: social influence and individual memory. Social influence represents the impact of opinions held by other agents in the network, while individual memory captures the retention of previously held beliefs. Specifically, each agent maintains an internal state representing their current belief, which is adjusted at each time step based on a proportion of the average opinion of their neighbors and a proportion of their own prior belief. This can be represented as $B_i(t+1) = \mu B_i(t) + (1-\mu)\frac{1}{k_i}\sum_{j \in N_i}B_j(t)$ , where $B_i$ is the belief of agent i, μ represents the strength of individual memory, and $k_i$ is the number of neighbors of agent i. The parameter μ determines the relative weighting of past beliefs versus the influence of others, effectively modeling the degree to which an individual is susceptible to external opinions.

Dynamic bias, as incorporated within the Friedkin-Johnsen Model, represents an individual’s slowly shifting, inherent predisposition towards certain opinions, independent of immediate social influence or memory of prior beliefs. This bias isn’t a static constant; instead, it evolves over time, influencing how individuals interpret and react to new information and the opinions of others. Mathematically, this is often represented as a time-varying parameter within the model, allowing for the simulation of collective behavior where underlying tendencies gradually change, impacting the overall convergence or polarization of opinions within a group. The inclusion of dynamic bias acknowledges that individuals are not simply passive recipients of social influence, but possess internally evolving factors that contribute to their belief systems and ultimately, collective dynamics.

Extracted human factors <span class="katex-eq" data-katex-display="false">\mathcal{H}_{D}^{T}</span> reveal attention <span class="katex-eq" data-katex-display="false">\mathcal{A}_{D}^{T}</span> patterns for major tech stocks. — Extracted human factors $\mathcal{H}_{D}^{T}$ reveal attention $\mathcal{A}_{D}^{T}$ patterns for major tech stocks.

HINTS in Practice: Amplifying Forecasting Accuracy

The Human-inspired Attention Network for Time Series (HINTS) consistently elevates the predictive power of existing forecasting architectures. When seamlessly integrated with established models – including PatchTST, DLinear, and TimeMixer – HINTS introduces a behavioral layer that refines the forecasting process. This synergistic approach doesn’t merely replace existing methods, but rather enhances their capabilities by incorporating human-centric dynamics. Rigorous testing demonstrates that HINTS consistently improves upon the baseline performance of these models across diverse datasets, providing a readily adaptable pathway to more accurate and nuanced time series predictions.

The HINTS framework refines forecasting by strategically focusing on the most pertinent aspects of human-derived behavioral signals. Rather than treating all indications of human influence equally, HINTS employs an attention mechanism to modulate the ‘Human Factor’ – a quantified representation of these signals. This process effectively filters out noise and emphasizes the behavioral patterns most strongly correlated with future outcomes. By selectively amplifying these crucial indicators, the framework enhances its ability to discern meaningful trends, ultimately leading to a marked improvement in forecast accuracy across diverse datasets and domains. This targeted approach allows HINTS to move beyond simply acknowledging human influence and instead harness it with precision, yielding more reliable and robust predictions.

Evaluations reveal the forecasting framework consistently elevates predictive performance across diverse applications. On the PEMS traffic dataset, the model achieves an impressive 28.9% improvement in accuracy, signifying enhanced ability to predict complex traffic patterns. This success extends to financial forecasting, where a 15.2% gain is observed using real-world datasets, and to the Exchange dataset, demonstrating a substantial 12.7% improvement. These results collectively highlight the framework’s adaptability and robustness, indicating its potential to deliver more accurate predictions in a variety of domains that demand reliable time-series analysis.

The core strength of this forecasting framework lies in its capacity to differentiate between genuine behavioral patterns and spurious data fluctuations. By effectively filtering out random noise, the system focuses on the underlying, intrinsic human dynamics that truly influence trends. This refined signal processing isn’t merely about identifying patterns; it’s about recognizing the meaningful patterns – those consistently driven by predictable human factors. Consequently, predictions generated by the framework demonstrate heightened robustness, proving less susceptible to short-term anomalies and yielding more reliable long-term forecasts across diverse datasets, from traffic flow to financial markets.

Beyond Prediction: Charting Future Directions

The core tenets of the Human INfluence Time-Series (HINTS) framework extend significantly beyond predictive forecasting, offering a versatile toolkit for diverse analytical challenges. Its ability to discern subtle shifts in sequential data, initially demonstrated in financial markets, proves readily adaptable to identifying anomalies in complex systems – from detecting fraudulent transactions to flagging unusual patterns in medical diagnostics. Furthermore, the framework’s emphasis on isolating human-driven influences makes it particularly valuable for behavioral analysis, allowing researchers to deconstruct the factors shaping individual or collective actions. This capacity to model and interpret nuanced behavioral signals holds considerable promise for advancements across the social sciences, offering new insights into areas like political polarization, consumer trends, and even the spread of misinformation, ultimately enabling a deeper understanding of human dynamics within complex systems.

Researchers are now directing efforts towards a more nuanced understanding of human behavior by expanding the HINTS framework to accommodate the interplay of multiple, simultaneous human factors. This progression moves beyond analyzing isolated influences and aims to model the complex web of motivations, biases, and environmental conditions that shape individual and collective actions. Furthermore, the potential for counterfactual analysis within HINTS is being explored, allowing investigators to assess “what if” scenarios – examining how alterations in specific factors might have led to different outcomes. This capability promises not only a deeper understanding of past events, but also the ability to proactively evaluate the potential consequences of interventions or policy changes, offering a powerful tool for informed decision-making in fields ranging from public health to organizational management.

Historically, the study of human behavior and the rigorous analysis of time-series data have remained largely separate endeavors. This framework, however, directly addresses this disconnect, offering a novel approach to understanding behavioral patterns not merely as static observations, but as dynamic processes unfolding over time. By applying the principles of time-series analysis – traditionally used in fields like economics and meteorology – to the complexities of human action, researchers gain the capacity to identify subtle precursors to significant events, model the influence of various contributing factors, and ultimately, move beyond simple description towards predictive understanding. This integration provides a powerful methodological advancement, potentially revolutionizing how social scientists approach questions ranging from individual decision-making to large-scale social trends, and revealing previously hidden relationships within the rich tapestry of human behavior.

The pursuit of accurate time-series forecasting, as demonstrated by HINTS, necessitates a holistic understanding of the underlying system. The framework’s ability to discern human-driven dynamics from residuals echoes a crucial principle of systemic design – that seemingly isolated components are intrinsically linked. Robert Tarjan aptly stated, “Complexity hides opportunity.” This resonates with HINTS’ approach; by dissecting the residual component-what remains after standard forecasting-the framework uncovers hidden influences driving the observed data. Just as one cannot replace the heart without understanding the bloodstream, HINTS recognizes that improvements in forecasting hinge on comprehending the entirety of the system’s behavioral patterns.

Looking Ahead

The pursuit of forecasting, much like city planning, often fixates on predicting the next skyscraper while neglecting the underlying infrastructure. This work, by focusing on the residuals – the unexplained variance in time-series data – offers a subtle but important redirection. To treat these residuals as a signal of human influence is not merely a statistical maneuver; it acknowledges that systems are not governed by equations alone, but by the complex interplay of agents within them. The Friedkin-Johnsen model provides a starting point, but true understanding demands a more nuanced grasp of how individual actions propagate through the network, and how those networks themselves evolve.

A critical limitation remains the assumption of a singular “human factor.” Real-world systems are rarely driven by a monolithic influence. Future research should explore methods for decomposing the residual signal into multiple, potentially competing, human-driven dynamics. Furthermore, the current reliance on attention mechanisms, while effective, feels somewhat akin to adding decorative facades to a structurally unsound building. A deeper investigation into the intrinsic structure of these residual signals – perhaps leveraging techniques from topological data analysis – may reveal more robust and interpretable patterns.

Ultimately, the challenge lies not in achieving ever-more-precise forecasts, but in building models that are resilient, adaptable, and capable of learning from the inevitable disruptions that characterize complex systems. The goal is not to predict the future, but to understand the principles that govern change, and to design systems that can evolve without requiring a complete demolition and rebuild of the entire block.

Original article: https://arxiv.org/pdf/2512.23755.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/