Author: Denis Avetisyan
A new system uses the power of artificial intelligence to visually assess time series predictions and flag potentially inaccurate results.
This paper introduces The Forecast Critic, a Large Language Model-based system for evaluating time series forecasts by incorporating visual inspection of historical and predicted data.
Accurate forecasting is critical for retail operations, yet traditional monitoring systems often struggle with nuanced errors beyond simple statistical deviations. This paper introduces ‘The Forecast Critic: Leveraging Large Language Models for Poor Forecast Identification’, a novel system employing Large Language Models (LLMs) to automatically assess time series forecast quality by reasoning about historical context and predicted values. Our results demonstrate that LLMs can reliably identify unreasonable forecasts-detecting issues like temporal misalignment or spurious spikes-and effectively incorporate unstructured data to refine these assessments, achieving F1 scores up to 0.88 without domain-specific training. Could this approach provide a scalable and insightful alternative to manual forecast evaluation, ultimately improving business outcomes?
The Futility of Prediction: Why Plausibility Matters
Conventional time series analysis, while foundational in forecasting, frequently falters when confronted with the intricacies of real-world data. These methods, often reliant on identifying and extrapolating past trends, assume a degree of stationarity – a consistent statistical behavior over time – that is rarely present in dynamic systems. External influences, such as unforeseen economic shifts, policy changes, or even viral events, introduce non-linearities and dependencies that traditional models – like simple moving averages or autoregressive integrated moving average (ARIMA) – struggle to capture. Consequently, predictions based solely on historical data can be significantly inaccurate, particularly during periods of rapid change or when faced with novel situations not reflected in past observations. This limitation underscores the need for more robust and adaptive forecasting techniques, or at least, a critical evaluation of model outputs against contextual plausibility.
Often sidelined in favor of purely statistical accuracy, the assessment of a forecast’s plausibility represents a vital, yet frequently neglected, stage in predictive modeling. A statistically ‘correct’ forecast – one minimizing error metrics – can still be deeply flawed if it defies common sense or established domain knowledge. For example, a model predicting a sudden, massive surge in demand for winter coats during a heatwave, while perhaps statistically plausible based on historical data, lacks contextual reasonableness. Evaluating plausibility demands incorporating external information, expert judgment, and an understanding of the underlying system’s constraints; it’s a critical safeguard against models extrapolating noise as signal, and ensures predictions are not only numerically sound, but also logically coherent within the real world.
The reliance on human judgment to validate forecasts introduces a significant bottleneck in automated systems. While a human can often intuitively identify an unrealistic prediction – a sudden, inexplicable spike in demand, for example – this subjective assessment is difficult to translate into algorithmic rules. Consequently, automated decision-making processes may proceed with implausible forecasts, leading to suboptimal or even erroneous outcomes. This isn’t merely a matter of inconvenience; in critical applications like supply chain management or energy grid stabilization, acting on forecasts that lack basic reasonableness can have substantial financial or logistical consequences. Bridging this gap requires developing computational methods capable of quantifying and incorporating contextual plausibility, moving beyond purely statistical measures of forecast accuracy to ensure predictions align with real-world expectations and constraints.
The Forecast Critic: A Sanity Check for Prediction
The Forecast Critic is a novel system designed to evaluate time series forecasts by utilizing the capabilities of Large Language Models (LLMs). Unlike traditional forecasting methods which generate predictions, The Forecast Critic functions as an assessment tool; it analyzes existing forecasts based on their visual characteristics as presented in time series plots. The system accepts time series data and corresponding forecasts as input, then employs an LLM to determine the plausibility of the forecast relative to the observed historical data. This LLM-driven approach enables evaluation without requiring knowledge of the forecasting method used to generate the forecast, offering a model-agnostic evaluation capability.
The Forecast Critic operates as an assessment tool, distinct from predictive models; it does not generate forecasts but rather evaluates the plausibility of existing ones. This evaluation is performed by analyzing the visual characteristics of a time series forecast – specifically, its adherence to established patterns and its contextual reasonableness when plotted against historical data. The system leverages Large Language Models to interpret these visual cues, identifying inconsistencies or improbable extrapolations that might not be flagged by traditional statistical error metrics like Mean Absolute Error or Root Mean Squared Error. This approach focuses on whether the forecast looks reasonable, given the data’s inherent behavior, offering a complementary layer of validation.
Traditional statistical measures of forecast accuracy, such as Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE), primarily focus on the magnitude of deviations between predicted and actual values. However, these metrics can fail to detect subtle but critical errors in forecast patterns or shapes, particularly in complex time series data exhibiting seasonality, trends, or irregular fluctuations. The Forecast Critic addresses this limitation by visually assessing forecast plausibility, enabling the identification of errors – like incorrectly predicted turning points or unrealistic rate of change – that would be overlooked by purely numerical evaluations. This visual approach facilitates a more holistic evaluation, considering both the accuracy and the overall reasonableness of the forecast in relation to the historical data’s behavior.
Demonstrating Reliability: A Rigorous Evaluation
Evaluation of the Forecast Critic incorporated both unaltered, or ‘clean’, forecast data and datasets deliberately modified to introduce specific error types. This dual approach enabled a granular assessment of the system’s sensitivity to different forecast inaccuracies. By comparing performance on clean data to performance on perturbed data, we quantified the system’s ability to reliably detect errors stemming from various sources, including those it had not been explicitly trained to identify. This methodology facilitated the measurement of false positive and false negative rates across a range of error conditions, providing a comprehensive understanding of the system’s robustness and limitations.
Evaluation of the Forecast Critic utilized synthetically generated time series data to assess performance under controlled conditions. These experiments demonstrated robust error detection, with the system achieving an F1 score of 0.88 when exposed to synthetic perturbations. This metric indicates a balance between precision and recall in identifying artificially introduced forecast errors within the generated data, confirming the system’s ability to reliably detect deviations from expected values in a controlled environment.
The Forecast Critic demonstrates accurate identification of common forecast errors, specifically trend shifts, translation errors, and unexpected spikes. Performance metrics, derived from experiments using synthetically generated time series data, indicate an F1 score of 0.97 when identifying these perturbations. This level of accuracy is comparable to that of a human baseline evaluator, suggesting the system’s capability to reliably detect and flag discrepancies between forecasted and actual data across these defined error types.
Beyond Numbers: Contextualizing Forecasts for Real-World Impact
Time series forecasting, traditionally focused on historical patterns, benefits substantially from the inclusion of external, or exogenous, variables. These features – encompassing events like promotional campaigns, holiday periods, or even broader economic indicators – provide crucial context often missing from models relying solely on past data. By integrating such information, forecasting accuracy and reliability are markedly improved, as the model gains the ability to anticipate the impact of specific, identifiable occurrences. This approach moves beyond simply predicting “what will happen next” based on past trends, to understanding why certain fluctuations occur, allowing for more nuanced and dependable predictions in complex, real-world scenarios.
The Chronos time series foundation model demonstrates a powerful capacity to integrate external contextual factors – such as promotional campaigns, holiday impacts, or even broader economic indicators – directly into its predictive process. Unlike traditional time series methods that often treat these influences as noise, Chronos actively learns how these exogenous variables correlate with and drive temporal patterns. This allows the model to generate forecasts that aren’t simply extrapolations of past behavior, but rather, nuanced predictions that reflect a more complete understanding of the forces at play. Consequently, the resulting forecasts exhibit increased realism and accuracy, particularly in dynamic environments where external events significantly shape outcomes, offering a substantial improvement over models that operate in isolation from such crucial context.
The Forecast Critic demonstrates a marked ability to evaluate forecast plausibility when external factors are considered, ultimately supporting more confident decision-making. Through rigorous testing involving the injection of exogenous features – such as promotional events – the system achieves a weighted F1 score of 0.83, indicating strong performance in distinguishing between reasonable and unreasonable predictions. Critically, a statistically significant difference in the scaled Continuous Ranked Probability Score (sCRPS) exists between forecasts flagged as implausible and those deemed reasonable, suggesting the system isn’t simply identifying randomness, but genuinely assessing the influence of these external events on predictive accuracy. This capacity to contextualize predictions elevates The Forecast Critic beyond a simple error metric, transforming it into a valuable tool for interpreting and trusting time series forecasts in dynamic environments.
The Forecast Critic, as detailed in the paper, attempts to inject a degree of reasoned judgment into the often-opaque world of time series forecasting. It’s a brave effort, really. The system visually inspects data, seeking inconsistencies-a pattern recognition exercise dressed up in LLM finery. One anticipates, however, that even the most sophisticated visual analysis will eventually encounter forecasts so divorced from reality that they defy logical assessment. As Donald Davies observed, “It is better to be thought a fool than to do a foolish thing.” The system may flag implausible predictions, but production will inevitably serve up edge cases that expose the limitations of even the cleverest algorithms. The core idea of incorporating exogenous information is sound, but every abstraction dies in production, and even beautifully rendered charts cannot always preempt the inevitable crash.
What’s Next?
The system presented here correctly identifies a forecast as ‘implausible’ with increasing frequency. This is, predictably, not the same as identifying a forecast as wrong. The bug tracker, already swollen with edge cases, will continue to accumulate examples of forecasts that are technically correct but economically useless. The pursuit of ‘plausibility’ offers a temporary reprieve from the tyranny of metrics, but it’s a reprieve built on sand. The model currently accepts exogenous information; soon, it will demand it, then complain that it isn’t granular enough.
The true limitation isn’t algorithmic. It’s human. The system surfaces anomalies, but assigning causation-distinguishing signal from noise, intention from incompetence-remains stubbornly manual. The promise of automated forecast evaluation will inevitably collide with the reality of messy data provenance and even messier organizational politics. It’s a sophisticated early warning system, but it doesn’t fix the underlying incentives that create poor forecasts.
The field will move toward ‘explainable implausibility’-a post-hoc rationalization for why a forecast failed, packaged as insight. This is not progress; it’s just re-branding. The system doesn’t deploy – it lets go.
Original article: https://arxiv.org/pdf/2512.12059.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-12-17 03:37