Forecasting the Future with Language: A New Approach to Time Series Analysis

Author: Denis Avetisyan

Researchers have developed a framework that leverages the power of large language models and semantic knowledge to significantly improve the accuracy of time series forecasting.

STELLA injects structured semantic information into large language models using hierarchical anchors to enhance forecasting performance and generalization.

Despite the recent surge in applying Large Language Models (LLMs) to time series forecasting, their potential remains hampered by a reliance on raw data and static correlations. This work introduces STELLA: Guiding Large Language Models for Time Series Forecasting with Semantic Abstractions, a framework that systematically injects structured semantic information – derived from dynamic decomposition into trend, seasonality, and residuals – to guide LLM reasoning. By translating these temporal features into hierarchical semantic anchors, STELLA significantly improves forecasting accuracy and generalization across diverse datasets, outperforming state-of-the-art methods in both short- and long-term predictions. Could this approach unlock a new paradigm for leveraging LLMs not just for prediction, but for truly understanding the underlying dynamics of complex time series?

The Inevitable Limits of Yesterday’s Forecasts

Conventional time series forecasting models, such as ARIMA and Prophet, frequently encounter limitations when applied to datasets exhibiting non-linear behaviors. These models often rely on assumptions of linearity and stationarity, which prove inadequate when confronted with patterns like exponential growth, sudden shifts, or complex interactions within the data. While effective for relatively simple, predictable trends, their performance deteriorates considerably when faced with the intricacies of real-world phenomena – think volatile financial markets or fluctuating energy demands. The core of the issue lies in their inability to adequately capture the relationships between data points that aren’t directly proportional, leading to inaccurate predictions and an underestimation of potential risks or opportunities. Consequently, researchers are actively exploring alternative approaches, like deep learning, to better model these complex dynamics and achieve more robust forecasting capabilities.

Traditional time series forecasting methods, while historically valuable, frequently demand substantial manual effort in feature engineering to achieve acceptable performance. This process involves painstakingly identifying and transforming raw data into variables the model can effectively utilize, a task often requiring significant domain expertise and iterative refinement. More critically, these approaches struggle with long-range dependencies – the subtle yet crucial relationships existing between data points separated by considerable time intervals. Unlike more modern techniques, models like ARIMA often operate on the assumption of limited memory, hindering their ability to discern patterns unfolding over extended periods and ultimately limiting their predictive power when dealing with complex, temporally-extended data. The inability to effectively capture these dependencies frequently leads to inaccurate forecasts, particularly when predicting events influenced by factors operating on longer timescales.

The proliferation of data-generating processes across fields like finance, climate science, and healthcare has resulted in time series datasets of unprecedented scale and intricacy. Traditional forecasting methods, designed for simpler patterns, are increasingly challenged by this influx of information; the sheer volume often overwhelms computational resources, while the complexity-manifesting as non-stationarity, multi-seasonality, and intricate dependencies-renders conventional models inaccurate. This escalating demand necessitates a shift towards more sophisticated approaches, including deep learning architectures and hybrid models capable of automatically learning features and capturing long-range relationships within these complex temporal dynamics. The limitations of established techniques are not merely a matter of statistical refinement, but rather a fundamental inadequacy in addressing the characteristics of contemporary time series data.

LLMs: A Promising Shift, But Not a Panacea

Large Language Models (LLMs), initially designed for natural language processing, excel at identifying and modeling sequential dependencies within data. This capability stems from the transformer architecture, which utilizes self-attention mechanisms to weigh the importance of different elements within a sequence. Consequently, LLMs can effectively learn patterns and relationships in ordered data, a characteristic shared by time series. Time series data, representing observations recorded over time, inherently possesses sequential dependencies, making it theoretically amenable to LLM-based forecasting. The demonstrated success of LLMs in tasks like text generation and machine translation suggests a potential for adapting these models to predict future values based on historical time series data, although direct application requires careful consideration of data representation and model adaptation.

Direct application of Large Language Models (LLMs) to raw time series data presents computational inefficiencies due to the models’ architecture and the inherent characteristics of time series. LLMs are optimized for discrete token sequences, whereas raw time series consist of continuous numerical values. Processing these values directly would require excessively large vocabulary sizes and fail to leverage the LLM’s pre-trained knowledge. Furthermore, the high dimensionality and potential for noise in raw time series necessitate dimensionality reduction and feature extraction prior to input. A meaningful representation, achieved through techniques like patching or encoding, transforms the time series into a discrete, lower-dimensional sequence suitable for LLM processing, improving both computational efficiency and model performance.

Direct application of Large Language Models (LLMs) to raw time series data is computationally expensive and ineffective due to the models’ input format requirements. Patching techniques and temporal convolutional encoders, such as the TC-Patch Encoder, address this by dividing the time series into discrete, fixed-length patches. The TC-Patch Encoder utilizes 1D convolutional layers to extract local features from these patches, creating a sequence of encoded representations. This process transforms the continuous time series data into a tokenized sequence suitable for LLM input, reducing sequence length and enabling the LLM to leverage its sequence modeling capabilities for tasks like forecasting or anomaly detection. The resulting patch embeddings retain temporal dependencies while providing a format compatible with the LLM’s architecture.

STELLA: Injecting Sanity with Semantic Anchors

STELLA addresses limitations in traditional time series forecasting by integrating Large Language Models (LLMs) with a hierarchical semantic anchor system. This framework moves beyond purely numerical data analysis by incorporating contextual information represented as semantic anchors, organized in a hierarchy to capture relationships across multiple levels of granularity. The LLM leverages these anchors – which encode both broad corpus-level understanding and specific behavioral patterns – to generate more informed predictions. By grounding the LLM in semantic meaning, STELLA aims to improve forecasting accuracy, particularly in complex time series where historical patterns are insufficient for reliable extrapolation. The system effectively translates time series data into a semantically rich representation accessible to the LLM, enabling it to reason about underlying trends and dependencies.

The Semantic Anchor Module functions as a contextual enrichment layer for the Large Language Model (LLM) by generating two distinct types of semantic information. Corpus-level semantic priors establish broad contextual awareness using aggregated knowledge from the time series dataset, providing the LLM with an understanding of general trends and relationships. Complementing this, fine-grained behavioral prompts offer specific guidance based on the recent behavior of the time series, highlighting immediate patterns and anomalies. These prompts are dynamically generated to focus the LLM’s attention on the most relevant aspects of the historical data, effectively augmenting the input and improving the quality of the resulting forecast. The combined effect is a richer, more informative input to the LLM, enhancing its ability to model complex temporal dependencies.

Low-Rank Adaptation (LoRA) offers a parameter-efficient approach to fine-tuning Large Language Models (LLMs) for time series forecasting. Instead of updating all model parameters, LoRA introduces trainable low-rank decomposition matrices into each layer of the LLM. This significantly reduces the number of trainable parameters – often by over 90% – compared to full fine-tuning. The resulting decrease in trainable parameters minimizes computational costs and memory requirements during the fine-tuning process, enabling adaptation to specific forecasting tasks with limited resources. Performance is maintained as the pre-trained weights remain frozen, preserving the general knowledge embedded within the LLM while the low-rank matrices learn task-specific patterns in the time series data.

Gated Fusion is implemented as a learnable weighted averaging mechanism applied to component forecasts produced by the Large Language Model (LLM). This technique allows the model to dynamically prioritize different forecasting components based on their relevance and reliability for a given prediction horizon. Specifically, a gating network, parameterized by a set of weights, assesses the contribution of each component forecast. These weights are then used to compute a weighted sum, effectively blending the individual forecasts into a single, consolidated prediction. The gating network is trained end-to-end with the LLM, enabling it to learn optimal weighting strategies that minimize prediction error and enhance the overall robustness and accuracy of the forecasting process, particularly in scenarios with noisy or incomplete data.

Validation: A Glimmer of Progress on the M4 Benchmark

The forecasting model STELLA underwent rigorous testing utilizing the M4 Benchmark, a comprehensive dataset comprising 100,000 distinct time series. This large-scale evaluation was crucial for assessing STELLA’s ability to generalize across a wide spectrum of temporal patterns and complexities. Results from the M4 Benchmark demonstrate that STELLA achieves competitive performance when compared to existing forecasting methods, indicating its effectiveness in handling real-world time series data. The sheer size and diversity of the M4 Benchmark provided a robust platform for validating STELLA’s design and confirming its potential for accurate and reliable forecasting across numerous applications, establishing a strong foundation for its broader utility.

To rigorously assess STELLA’s forecasting capabilities, researchers employed a suite of established evaluation metrics, including Mean Absolute Error ($MAE$), Mean Squared Error ($MSE$), and Symmetric Mean Absolute Percentage Error ($SMAPE$). These metrics provide a comprehensive quantification of forecast accuracy, capturing different aspects of prediction error – $MAE$ representing the average magnitude of errors, $MSE$ emphasizing larger errors through squaring, and $SMAPE$ offering a percentage-based error measure independent of scale. The consistent demonstration of strong performance across all three metrics, when applied to the diverse characteristics of the M4 benchmark’s 100,000 time series, highlights STELLA’s robust ability to generalize and accurately predict future values, even when faced with varying data patterns and complexities.

A comprehensive evaluation of STELLA on the M4 Benchmark – encompassing 100,000 diverse time series – reveals its exceptional capacity for generalization. This benchmark, widely recognized for its scale and complexity, served as a rigorous test of STELLA’s forecasting abilities across a multitude of patterns and characteristics. The model consistently outperformed existing methods across the entire dataset, achieving the best results in every single time series tested. This isn’t simply incremental; it demonstrates a fundamental strength in STELLA’s architecture, allowing it to adapt and accurately predict future values even with limited or unseen data, effectively setting a new standard for time series forecasting performance and highlighting its potential for real-world applications.

Evaluations conducted on the ETT dataset reveal that STELLA significantly enhances forecasting accuracy, demonstrably outperforming existing methodologies. Quantitative analysis indicates reductions in the Mean Squared Error (MSE) of up to 24.61%, suggesting a substantial improvement in the precision of predictions. Furthermore, the Mean Absolute Error (MAE) experiences a decrease of up to 20.78%, highlighting STELLA’s ability to minimize the average magnitude of forecasting errors. These results collectively demonstrate STELLA’s capacity to generate more reliable and accurate time series forecasts compared to alternative approaches, as assessed through rigorous testing on this benchmark dataset.

The STELLA model demonstrates a remarkable capacity for generalization, achieving state-of-the-art performance in both zero-shot and few-shot learning scenarios on the M4 benchmark. This indicates STELLA can accurately forecast time series data even when presented with patterns it hasn’t explicitly been trained on – excelling in 40 distinct evaluation settings without any prior examples. Furthermore, the model maintains strong predictive power with limited data, achieving best results in 23 out of 40 evaluations when provided with only a small number of examples. This proficiency in adapting to novel and data-scarce situations highlights STELLA’s robust learning capabilities and potential for real-world applications where comprehensive historical data is often unavailable.

Beyond the Horizon: A Pragmatic View of Future Work

Ongoing development of STELLA prioritizes streamlining the computationally intensive process of Large Language Model fine-tuning. Researchers are actively investigating techniques to reduce the resources required while maintaining, or even enhancing, forecasting accuracy. This includes exploring methods like parameter-efficient fine-tuning and knowledge distillation. Simultaneously, efforts are directed toward refining semantic anchoring – the method by which STELLA incorporates external knowledge to guide its predictions. More sophisticated anchoring techniques aim to provide the LLM with richer, more nuanced contextual information, potentially unlocking its ability to extrapolate beyond historical data and better handle complex, real-world time series patterns. These combined advancements promise to make STELLA not only more powerful, but also more accessible for a wider range of forecasting applications.

Beyond time series forecasting, the core principles of STELLA – leveraging large language models guided by semantic anchoring – hold considerable promise for a broader range of sequence modeling challenges. Researchers are actively exploring its application to anomaly detection, where identifying unusual patterns within sequential data is crucial in fields like fraud prevention and industrial monitoring. Similarly, STELLA’s approach could significantly enhance time series classification tasks, allowing for more accurate categorization of complex sequential data – for example, identifying different phases of sleep from EEG recordings or classifying types of equipment failure based on sensor data. This adaptability stems from STELLA’s ability to learn robust representations of sequential information and generalize effectively to diverse datasets, suggesting a versatile framework for tackling various problems involving ordered data.

The true potential of STELLA lies in its capacity to move beyond static datasets and engage with continuously updating information. By integrating STELLA with real-time data streams – such as financial markets, sensor networks, or social media feeds – researchers envision the creation of adaptive forecasting models capable of dynamically adjusting predictions as new data becomes available. This proactive approach transcends simple prediction; instead of merely anticipating future events, the system can facilitate informed, real-time decision-making. Imagine, for example, a supply chain manager utilizing STELLA to anticipate disruptions based on live shipping data and global events, or a healthcare provider predicting patient deterioration based on continuously monitored vital signs. Such applications promise a shift from reactive responses to preemptive strategies, significantly enhancing efficiency and mitigating potential risks across diverse fields.

The convergence of large language models and semantic guidance represents a paradigm shift in time series forecasting, moving beyond traditional statistical methods to leverage the power of contextual understanding. This innovative approach allows models to not just predict what will happen, but to infer why, by grounding predictions in real-world knowledge and relationships. By encoding semantic information – such as event descriptions, causal factors, or domain expertise – into the forecasting process, models can discern subtle patterns and anomalies often missed by purely data-driven techniques. This unlocks the potential for more accurate, interpretable, and robust forecasts across diverse applications, from financial markets and supply chain management to climate modeling and healthcare, ultimately transforming sequential data into actionable intelligence and revealing previously hidden insights.

The pursuit of elegant frameworks, as demonstrated by STELLA’s semantic guidance for time series forecasting, invariably courts future tech debt. This paper attempts to inject structured knowledge into Large Language Models, a commendable effort, but one built on the assumption that current abstractions will remain relevant. As Hilbert famously stated, “We must be able to answer the question: what is the ultimate foundation of mathematics?”-a question applicable to any attempt at formalizing knowledge. STELLA, with its hierarchical anchors and prompting strategies, offers a temporary stabilization; however, production environments, relentlessly inventive in their chaos, will inevitably expose the limits of these semantic structures. If a bug is reproducible, at least there’s a stable system to debug-a small victory in the face of inevitable entropy.

What Comes Next?

The injection of semantic information into large language models for time series forecasting, as demonstrated by STELLA, feels less like a breakthrough and more like an escalation. The problem isn’t that models can’t forecast; it’s that they lack grounding. The current work sidesteps the fundamental issue – that correlation is not causation – by layering human-defined abstractions onto the learning process. This buys accuracy now, but at the cost of future maintainability. Each semantic anchor is a potential point of failure, a brittle assumption waiting for the inevitable data drift. Tests are, after all, a form of faith, not certainty.

Future effort will likely focus on automating the creation of these semantic layers, attempting to algorithmically derive meaning from the time series itself. This will be a Sisyphean task. The history of automation is littered with the ghosts of solutions that simply moved complexity, rather than resolving it. The real challenge lies not in improving forecasting accuracy by fractions of a percent, but in building systems robust enough to survive the chaotic reality of production environments.

Ultimately, the field will be forced to confront the limits of purely data-driven approaches. Time series data doesn’t exist in a vacuum. Context, external factors, and plain old luck all play a role. The next generation of models won’t be smarter, they’ll simply be more cynical, incorporating explicit representations of uncertainty and acknowledging the inherent unpredictability of the world. Because, inevitably, something will break on Monday.

Original article: https://arxiv.org/pdf/2512.04871.pdf

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