Seeing What the Data Says: A New Dataset Bridges Time Series and Language

Author: Denis Avetisyan

Researchers have created a large-scale dataset that links time series data with natural language descriptions, unlocking new possibilities for automated analysis and interpretation.

Statistical tools facilitate the creation of trend datasets, enabling systematic observation and analysis of evolving patterns.

This work introduces TS-Insights, enabling multimodal models to accurately analyze time series data and generate corresponding natural language explanations through instruction tuning.

Despite the prevalence of time series data across diverse fields, extracting meaningful insights typically demands specialized domain expertise. This limitation motivates the work presented in ‘Insight Miner: A Time Series Analysis Dataset for Cross-Domain Alignment with Natural Language’, which introduces TS-Insights, a large-scale dataset aligning time series with natural language descriptions. Demonstrating the efficacy of this approach, finetuning multimodal models on TS-Insights significantly improves their ability to accurately describe and interpret time series data. Could this represent a crucial step toward enabling large language models to natively understand and reason with temporal information?

The Challenge of Interpretable Time Series

While techniques like ARIMA and State Space Models demonstrate remarkable proficiency in forecasting future values within a time series, their outputs often present a challenge for human comprehension. These methods primarily focus on mathematical relationships and statistical correlations, delivering predictions without inherent explanations of why those predictions are made. A model might accurately anticipate a surge in sales, for example, but offer no insight into the underlying drivers – be it a marketing campaign, seasonal demand, or external economic factors. This lack of interpretability limits the actionable intelligence derived from the data; stakeholders require not just what will happen, but a clear understanding of the forces shaping the outcome to inform strategic decisions and effectively respond to change. Consequently, the full potential of time series analysis remains unrealized when predictive power isn’t coupled with readily understandable narratives.

Decomposition of a time series, frequently achieved through Seasonal-Trend decomposition using Loess (STL), is a foundational step in analysis, separating observations into constituent parts – the underlying trend, repeating seasonal patterns, and unpredictable residuals. However, simply identifying these components is insufficient for practical understanding; translating these mathematical decompositions into coherent, human-interpretable narratives proves remarkably challenging. While STL precisely quantifies cyclical behavior or long-term shifts, discerning the meaning behind these patterns-what drives the trend, the cause of seasonal fluctuations, or the significance of residual noise-requires domain expertise and careful consideration. Without bridging this gap between statistical output and contextual understanding, the valuable information embedded within time series data risks remaining obscured, limiting its utility for informed decision-making.

The practice of focusing on specific, contiguous portions of a time series – known as analyzing through a ‘Time Series Window’ – has become increasingly vital for deriving meaningful insights. Rather than treating a dataset as a monolithic whole, this approach allows for the identification of localized patterns, anomalies, or shifts in behavior that might otherwise be obscured. Effective interpretation at this scale demands tools and techniques capable of distilling complex fluctuations within each window into understandable narratives; for instance, a sudden spike in sales within a specific week, or a gradual decline in website traffic over a month. This segmented analysis isn’t simply about pinpointing events, but understanding why those events occurred within that particular timeframe, and how they differ from behavior observed in other windows. Consequently, the ability to interpret time series data through this localized lens unlocks a more granular and actionable understanding of underlying processes, moving beyond prediction to genuine comprehension.

The true power of time series data frequently remains untapped due to a significant hurdle: the difficulty in extracting readily understandable insights. While volumes of data can be collected and statistically modeled, a lack of clear interpretation limits its practical application; predictions, however accurate, are insufficient without contextual understanding. Businesses struggle to translate patterns in sales figures, website traffic, or sensor readings into actionable strategies, and scientists may overlook critical relationships hidden within complex environmental or physiological datasets. This interpretability gap hinders effective decision-making, preventing organizations and researchers from fully capitalizing on the wealth of information embedded within these sequential observations; ultimately, the value of time series data is directly proportional to the ease with which its underlying stories can be revealed and applied.

LMMs: Bridging Data and Narrative

Large Multimodal Models (LMMs) represent a departure from traditional time series analysis methods by integrating numerical data with descriptive natural language. Historically, time series data has been primarily analyzed using statistical techniques and machine learning algorithms focused solely on the numerical sequences. LMMs, however, enable the incorporation of textual information – such as contextual details, event descriptions, or metadata – alongside the time series values. This allows the model to not only identify patterns and make predictions based on the data itself, but also to understand the data within a broader context, potentially improving accuracy and interpretability. The combined processing of both modalities enables a more holistic analysis, where the model can leverage relationships between the numerical trends and the associated textual descriptions, facilitating the generation of human-understandable insights.

Large Language Models (LLMs) such as LLaVA and GPT-2, originally designed for image and text processing, are being adapted for time series analysis through techniques like visual prompt engineering and data encoding. LLaVA, a multimodal model, can accept time series data represented as images (e.g., line charts) alongside text prompts, allowing it to generate descriptions or answer questions about the data. GPT-2, a text-generation model, can be utilized by converting time series data into a textual representation, effectively framing the numerical data as a language modeling task. This adaptation involves fine-tuning these models on time series datasets or leveraging their existing capabilities through carefully crafted prompts, enabling them to interpret patterns and generate human-readable insights without requiring complete retraining for time series specific tasks.

Large Multimodal Models (LMMs) demonstrate inference capabilities in time series analysis through two primary approaches: zero-shot and few-shot learning. Zero-shot inference allows the LMM to generate descriptive analyses of time series data without any specific prior training on time series datasets; the model relies on its pre-existing knowledge from training on broader datasets. Alternatively, few-shot inference enhances performance by providing the LMM with a limited number of example time series and their corresponding descriptions; this small dataset guides the model in adapting its existing knowledge to the specific characteristics of the target time series, improving the accuracy and relevance of generated insights. Both methods avoid the need for extensive, labeled time series data, reducing the computational cost and time associated with traditional machine learning approaches.

Effective training of Large Multimodal Models (LMMs) for time series data requires a methodology focused on aligning numerical data with corresponding natural language representations. This involves constructing datasets where time series are paired with detailed, human-written descriptions of observed patterns, anomalies, or forecasted trends. Training then utilizes techniques like contrastive learning or next-token prediction to optimize the LMM’s ability to generate accurate and relevant descriptions given new time series inputs. Successful models demonstrate an ability to not only identify key features – such as seasonality, trend, or outliers – but also to articulate these features in a manner understandable to a human analyst, facilitating quicker and more informed decision-making. The quality of the training data, specifically the granularity and accuracy of the descriptive language, is a primary determinant of the model’s performance.

TS-Insights: A Dataset for Meaningful Interpretation

The TS-Insights Dataset was created to mitigate the lack of labeled data available for training Large Multimodal Models (LMMs) in time series analysis. This dataset consists of 100,000 time series, each paired with natural language descriptions detailing key characteristics. These descriptions explicitly capture the time series’ underlying trend – whether increasing, decreasing, or stable – as well as any present seasonality and the nature of the residual noise. This alignment of quantitative time series data with qualitative descriptive language provides the necessary training signal for LMMs to learn the relationship between time series patterns and their human-interpretable explanations.

The TS-Insights Dataset comprises 100,000 distinct time series, each paired with a corresponding natural language description. This pairing facilitates the training of Large Multimodal Models (LMMs) by establishing a direct correlation between numerical time series data and human-interpretable language. The dataset’s scale is intended to provide sufficient data volume to overcome limitations imposed by data scarcity in time series analysis and enable robust model generalization. Each time series within the dataset is accompanied by a textual annotation detailing observed patterns, anomalies, or overall trends, forming the basis for supervised learning and model evaluation.

The TS-Insights Dataset facilitates the generation of human-understandable insights from time series data by establishing a direct correspondence between numerical time series and descriptive natural language. This alignment allows Large Language Models (LMMs) to move beyond simply identifying patterns in data – such as trend, seasonality, or anomalies – and to articulate those patterns in a way that is easily interpretable by humans. The dataset’s structure enables LMMs to learn the relationship between specific time series characteristics and their corresponding linguistic descriptions, effectively bridging the gap between quantitative data and qualitative understanding. Consequently, LMMs can translate complex time series data into accessible narratives, providing explanations and interpretations without requiring specialized analytical expertise.

The TS-Insights Dataset facilitates the development of specialized Large Multimodal Models (LMMs) for time series analysis, exemplified by ‘Insight Miner’. This model is a fine-tuned version of LLaVA, optimized for interpreting time series data and generating corresponding insights. Quantitative evaluations demonstrate that Insight Miner achieves performance levels competitive with GPT-4 on tasks involving time series understanding and description, indicating the dataset’s effectiveness in bridging the gap between numerical data and human-interpretable language. The model’s capabilities include identifying trends, anomalies, and seasonal patterns within time series and articulating these findings in natural language.

From Prediction to Comprehension: A New Era in Time Series Analysis

The current generation of time series analysis is evolving beyond mere forecasting, driven by the integration of Large Multimodal Models (LMMs) and comprehensive datasets like TS-Insights. This approach doesn’t simply predict what will happen, but aims to elucidate why patterns emerge within complex data. By processing time series data alongside related contextual information-such as news events or economic indicators-LMMs can identify the underlying drivers and relationships influencing trends. This shift towards proactive understanding enables a more nuanced interpretation of data, moving past statistical correlation to reveal causal mechanisms and providing actionable insights for data scientists and analysts seeking to anticipate and respond to dynamic changes.

FinVis-GPT exemplifies the potential of large multimodal models when applied to the complexities of financial data. This model doesn’t merely identify patterns within charts and datasets; it actively interprets the visual information, contextualizes it with numerical data, and generates human-readable explanations of observed trends. Through its analysis, FinVis-GPT can articulate the reasoning behind market fluctuations, pinpoint key drivers influencing asset prices, and even summarize complex financial reports in a concise and understandable manner. This capability moves beyond traditional quantitative analysis, offering a qualitative layer of understanding that empowers analysts to not only see what happened, but also to comprehend why it happened, ultimately leading to more informed and strategic decision-making.

Evaluations reveal that Insight Miner demonstrates a superior capacity for interpreting time series data compared to leading large language models. Domain experts assessed Insight Miner’s descriptive abilities with a score of 0.82, a statistically significant improvement over GPT-4’s 0.73 on independent holdout datasets. This metric, the Description Evaluation Score, quantifies the model’s ability to not just identify patterns, but to articulate meaningful explanations of the observed trends – a crucial step towards truly understanding the underlying dynamics within complex time series. The results suggest Insight Miner offers a more nuanced and reliable interpretation, potentially enabling more informed and effective data-driven decisions.

The true value of advanced time series analysis now extends beyond simply anticipating what will happen; it delves into explaining why. Current methodologies empower data scientists and analysts to not only forecast future trends with increasing accuracy, but also to articulate the underlying reasons driving those projections. This shift from prediction to comprehension is crucial, as understanding the causal factors behind a trend allows for more informed and robust decision-making. Rather than reacting to forecasted outcomes, professionals can proactively adjust strategies based on a clear understanding of the forces at play, mitigating risks and capitalizing on opportunities with greater confidence. This capability transforms data analysis from a reactive exercise into a proactive tool for strategic advantage.

The creation of TS-Insights exemplifies a dedication to paring down complexity in data analysis. The dataset’s focus on aligning time series with natural language isn’t merely about enhancing model capabilities; it’s about extracting signal from noise, a principle central to effective design. As Brian Kernighan observed, “Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it.” This sentiment resonates deeply with the approach taken in crafting TS-Insights; the elegance lies not in intricate algorithms, but in a clear, accessible representation of complex temporal data, allowing for simpler, more robust analysis and ultimately, easier ‘debugging’ of trends.

Further Refinements

The creation of TS-Insights addresses a conspicuous gap, yet reveals the inherent messiness of aligning quantitative and qualitative realms. The dataset’s efficacy, while promising, merely postpones the fundamental question: what constitutes a ‘correct’ natural language description of a time series? The current paradigm leans heavily on human annotation, a process susceptible to subjective interpretation and, ultimately, limited scalability. Future iterations should explore methods for generating synthetic, yet rigorously verifiable, descriptions-a move toward objective truth, however elusive.

A persistent challenge lies in the decomposition of ‘insight’ itself. The current framework focuses on trend identification and description. However, time series data often contains nuanced anomalies, seasonality, and complex interdependencies. The field must move beyond surface-level observations toward models capable of inferring causal relationships and predicting future states with quantifiable uncertainty-a shift from description to genuine understanding.

The elegance of a solution, it is often said, lies in its simplicity. The proliferation of multimodal architectures, while impressive, risks obscuring the core problem. Perhaps the true path forward involves refining existing time series analysis techniques-algorithms honed over decades-and grafting them onto large language models, rather than attempting to subsume the former within the latter. A little subtraction, after all, can reveal a great deal.

Original article: https://arxiv.org/pdf/2512.11251.pdf

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

The Challenge of Interpretable Time Series

LMMs: Bridging Data and Narrative

TS-Insights: A Dataset for Meaningful Interpretation

From Prediction to Comprehension: A New Era in Time Series Analysis

Further Refinements

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