Unlocking Transformer Forecasts: A New Approach to Explainable AI

Author: Denis Avetisyan

Researchers have developed a novel method for interpreting the predictions of Transformer models used in time series forecasting, offering valuable insights into how these complex systems arrive at their conclusions.

The system forecasts future states by masking portions of input time-series data, then quantifying feature importance through Shapley values-calculated as the difference in prediction with and without specific feature groups-to reveal both global feature dependencies and contributions to local explanations.

This paper introduces SHAPformer, a sampling-free method for calculating SHAP values to enhance the explainability and accuracy of Transformer-based time series forecasting, demonstrated with electrical load data.

While time-series forecasting is crucial for informed decision-making, a lack of transparency often hinders trust and practical adoption. This paper introduces ‘Explainable time-series forecasting with sampling-free SHAP for Transformers’, presenting SHAPformer, a novel Transformer-based model designed to address this challenge. By leveraging attention manipulation and a sampling-free approach to calculating SHAP values, SHAPformer delivers significantly faster and more accurate explanations of model predictions. Can this approach unlock broader deployment of time-series forecasting in sensitive applications demanding both performance and interpretability?

The Inevitable Shadows of Prediction

The ability to accurately predict future events based on historical data – time series forecasting – underpins crucial decision-making across diverse fields, from financial markets and supply chain management to healthcare and environmental monitoring. However, many contemporary forecasting models, while achieving impressive predictive power, operate as “black boxes,” offering little insight into why a particular prediction was made. This lack of transparency isn’t merely an academic concern; it directly hinders trust in the forecasts and, crucially, limits their practical application. Without understanding the factors driving a prediction, stakeholders are less likely to act upon it, and the potential for informed intervention or proactive adjustments is significantly diminished. Consequently, a growing emphasis is being placed on developing forecasting techniques that balance predictive accuracy with interpretability, enabling users to not only anticipate future trends but also to confidently understand and leverage the underlying reasoning.

While techniques like Linear Regression and XGBoost Regressor have long been favored for their transparency – readily revealing the influence of each input feature on predictions – these methods frequently struggle when confronted with the intricacies of real-world time series data. These traditional statistical approaches assume relatively simple, linear relationships, and their performance diminishes considerably as systems exhibit non-linear behaviors, such as accelerating growth, sudden shifts, or complex interactions between variables. Consequently, although easily understood, models built upon these foundations may offer inaccurate forecasts when applied to the dynamic and often unpredictable nature of many time-dependent phenomena, prompting a search for more powerful, albeit less interpretable, alternatives.

Recent advancements in time series forecasting have been significantly driven by deep learning models, notably those leveraging Transformer architectures originally designed for natural language processing. These networks demonstrate a remarkable ability to capture complex, non-linear dependencies within sequential data, often surpassing the predictive power of traditional statistical methods. However, this improved accuracy comes at a cost: the inherent complexity of these models creates a “black box” effect, making it difficult to understand why a particular forecast was generated. This lack of transparency poses a substantial barrier to adoption, particularly in critical applications where trust and the ability to validate predictions are paramount; stakeholders are often hesitant to rely on forecasts they cannot interpret or explain, hindering the practical implementation of these powerful, yet opaque, systems.

SHAPformer, a Transformer model, effectively explains real-world load data from TransnetBW, revealing feature importance and dependencies-highlighting the strongest interacting variables through dependence plots and feature importance scores calculated with the Permutation Explainer and Custom Masker.

Unveiling the Logic Within: Explainable AI and SHAP Values

Explainable AI (XAI) addresses the lack of interpretability in many machine learning models, particularly complex ones. SHAP (SHapley Additive exPlanations) values represent a specific approach to XAI by leveraging concepts from cooperative game theory to assign each feature an importance value for a particular prediction. These values quantify the marginal contribution of each feature to the difference between the actual prediction and the average prediction. The resulting SHAP values, calculated consistently based on Shapley values, provide a unified measure of feature importance that is both locally (for a single prediction) and globally (across the entire dataset) interpretable. This allows for a granular understanding of model behavior and facilitates trust in model outputs by revealing the reasoning behind individual predictions.

SHAP (SHapley Additive exPlanations) values operate on the principle of game theory to assign each feature an importance value for a particular prediction. Specifically, a SHAP value represents the average marginal contribution of a feature across all possible combinations of other features. This decomposition allows stakeholders to understand how each input variable influenced the model’s output; a positive SHAP value indicates the feature pushed the prediction higher, while a negative value indicates it pushed it lower. By quantifying these contributions for individual predictions, SHAP values provide local explanations of model behavior, fostering transparency and enabling users to assess the reliability and fairness of the model’s decisions. This detailed attribution is critical for building trust in machine learning systems, particularly in high-stakes applications.

The computational cost of determining SHAP values scales with both model complexity and dataset size. Calculating SHAP values requires running the model multiple times – once for each feature, and for various combinations of features – to determine each feature’s marginal contribution to the prediction. For complex models like deep neural networks or gradient boosting machines, each model run is itself computationally intensive. With large datasets, the number of required model runs increases linearly with the number of samples, quickly becoming prohibitive. Approximations, such as KernelSHAP or TreeSHAP, are often employed to reduce this computational burden, but these approximations introduce a trade-off between speed and accuracy in the resulting SHAP values. Therefore, while theoretically powerful, the practical application of SHAP values is frequently constrained by available computational resources.

SHAPformer accurately estimates feature importance on synthetic data, revealing strong interactions-particularly with the most influential feature-and demonstrating its ability to capture complex relationships between variables.

SHAPformer: A System Designed for Explanation

SHAPformer is a Transformer-based forecasting model specifically engineered for efficient Shapley Additive exPlanations (SHAP) value calculation. The model utilizes Masked Attention, a modification to the standard self-attention mechanism, to restrict attention only to relevant input features during the forecasting process. This targeted attention reduces computational overhead. Furthermore, SHAPformer employs Feature Grouping, which aggregates similar input features to minimize redundant SHAP value calculations. By combining these techniques, SHAPformer aims to provide interpretable forecasts with significantly reduced computational cost compared to traditional SHAP estimation methods.

SHAPformer reduces the computational expense of SHAP value estimation through two primary mechanisms. First, it employs a Masked Attention strategy that limits the Transformer’s attention scope to only the most relevant features for each prediction, thereby decreasing the number of feature combinations considered during SHAP calculation. Second, the model groups similar input features together, effectively reducing the total number of features requiring individual permutation-based importance assessments. This grouping allows SHAP values to be calculated for feature groups rather than individual features, further accelerating the process and mitigating the combinatorial explosion inherent in traditional SHAP estimation techniques.

Evaluations utilizing a synthetic dataset indicate that SHAPformer maintains forecasting accuracy comparable to, and in some cases exceeding, that of standard forecasting models. Critically, SHAPformer substantially reduces the time required for SHAP value calculation; benchmark testing demonstrates inference speeds are 50 to 800 times faster than those achieved by the Permutation Explainer and Custom Masker methods. This performance gain is achieved without compromising the fidelity of the explanations, allowing for efficient interpretability in time series forecasting applications.

Refining the Signal: Advanced Techniques for SHAP Estimation

Significant gains in the efficiency of SHAP value calculations are realized through innovative techniques like the Custom Masker and the implementation of Owen values. The Custom Masker strategically focuses computations on the most informative subsets of features, drastically reducing the overall processing time. Simultaneously, the adoption of Owen values-a sophisticated approach to approximating expected values-offers a compelling alternative to traditional calculations, enhancing both the speed and stability of SHAP estimations. These advancements build directly upon the foundations laid by SHAPformer, a method known for its optimized computations, and collectively enable more comprehensive and reliable feature importance analysis, particularly within complex datasets where computational resources may be constrained.

Refinements to SHAP value estimation go beyond mere computational efficiency, fundamentally bolstering the precision and dependability of these critical feature importance metrics. By minimizing instability in the calculations, these enhancements ensure that observed feature contributions are not simply artifacts of the estimation process itself. This increased reliability translates directly into more trustworthy insights, allowing analysts to confidently identify the drivers behind model predictions and build a more robust understanding of complex data. Consequently, decisions informed by these refined SHAP values are less susceptible to misinterpretation, fostering greater confidence in the interpretability and trustworthiness of machine learning models.

A particularly robust approach to discerning feature importance within time series data leverages Permutation Explainers in conjunction with optimized SHAP estimation techniques. This methodology assesses feature contributions by systematically shuffling the values of a single feature and observing the resulting change in model performance; a substantial drop indicates a critical feature. By integrating Permutation Explainers with enhancements like the Custom Masker and Owen Values, the process becomes more efficient and reliable across varied datasets. The resulting framework doesn’t rely on assumptions about model linearity or feature independence, making it uniquely suited to capture complex interactions and non-linear relationships often present in time series analysis, ultimately providing a more nuanced understanding of what drives model predictions.

Permutation Explainer and Custom Masker successfully generate local explanations for TransnetBW examples, highlighting feature importance.

Towards a Future of Transparent Time Series Intelligence

The convergence of SHAPformer and refined SHAP estimation-particularly through innovations like the Custom Masker-marks a considerable advancement in the pursuit of dependable and understandable time series intelligence. SHAPformer, built upon the Transformer architecture, inherently offers the capacity to capture complex temporal dependencies; however, its interpretability is greatly enhanced by applying SHAP values. These values quantify the contribution of each feature-or, crucially, each historical time step-to a specific forecast. The Custom Masker further refines this process by addressing the challenges of feature dependencies within time series data, ensuring that SHAP values are accurately assigned even when variables are correlated. This results in models that not only predict future values with precision but also provide clear, actionable insights into why those predictions are made, fostering trust and enabling informed decision-making in a variety of applications.

The methodologies developed are poised for impactful deployment across critical real-world applications. Initial efforts will concentrate on electrical load forecasting, a field demanding both accuracy and reliable predictions to optimize energy distribution and grid stability. Simultaneously, researchers are directing these techniques toward financial time series analysis, where the ability to interpret forecast drivers – beyond simply predicting values – is crucial for risk management and informed investment strategies. These applications represent significant challenges, requiring models to navigate complex, non-stationary data; success in these areas will demonstrate the practical value and scalability of the developed interpretable time series intelligence framework.

Although the SHAPformer model requires a considerably longer training period – ranging from two to ten times that of typical Temporal Fusion Transformer or Transformer architectures – the investment yields significant advantages. The increased computational effort unlocks highly interpretable forecasts, allowing for a clear understanding of the factors driving predictions. Critically, this enhanced interpretability is coupled with substantial gains in inference speed, meaning predictions can be generated far more quickly once the model is trained. This combination of clarity and efficiency opens doors to novel applications in time series analysis, particularly in scenarios where both accuracy and understanding are paramount, and where rapid decision-making is essential.

Permutation Explainer and Custom Masker dependence plots on the TransnetBW dataset reveal feature importance, with added noise to discrete variables for improved visualization.

The pursuit of model interpretability, as demonstrated by SHAPformer, echoes a fundamental tension. It isn’t simply about explaining predictions, but acknowledging the inherent limitations of any system designed to model complex temporal dependencies. The model, even with efficient SHAP value calculations, remains a prophecy of future failure, a simplification of reality. As Marvin Minsky observed, “You can’t always get what you want, but you can get what you need.” This sentiment resonates deeply; SHAPformer doesn’t promise perfect understanding, but provides a necessary tool for navigating the complexity of time-series forecasting, particularly in critical applications like electrical load prediction. Scalability, in this context, isn’t about speed – it’s about accepting that every optimization trades flexibility for a transient advantage.

What Lies Ahead?

The pursuit of explainability, as demonstrated by this work, isn’t a quest for perfect mirrors – reflecting a model’s reasoning with flawless fidelity. It is, instead, an exercise in controlled forgiveness. A system isn’t built; it’s cultivated. The efficient calculation of SHAP values for Transformers, while a practical advancement, merely illuminates the edges of the darkness. The true challenge isn’t explaining how a forecast arrived, but accepting that the garden will always contain weeds – unexpected feature interactions, emergent behaviors, and the inherent opacity of complex systems.

Future work will inevitably focus on scaling these explanation methods to even larger models and more intricate datasets. Yet, the real leverage may lie in shifting the question entirely. Instead of seeking post-hoc explanations, could attention mechanisms themselves be designed with inherent interpretability? Perhaps a focus on building models that invite understanding, rather than demanding it after the fact. A system that broadcasts its vulnerabilities, rather than concealing them.

The electrical load forecasting domain offers a tempting simplicity, but true tests will come from applying these techniques to systems with genuine agency – those that adapt, learn, and evolve beyond initial design. For in those gardens, the weeds aren’t bugs; they’re new species, and the art of forecasting becomes the art of coexistence.

Original article: https://arxiv.org/pdf/2512.20514.pdf

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