The Vision of Predictive Technology

Imagine what predictive technology could look like. When the foundational capabilities of general large models, the precision of specialized predictive models, the practical value of external tools, and the assurance of trustworthy mechanisms are organically integrated, artificial intelligence will become a reliable “future advisor” in critical areas such as financial risk control, weather forecasting, public governance, and industrial production. This integration will provide wisdom to guide humanity in understanding future trends and become a significant force in empowering social development and serving the modernization of national governance.

Four Technical Paths for Future Prediction

Faced with increasingly complex predictive demands in the real world, researchers have developed two core lines and four specific technical paths around large model predictive technology. These paths are not competing alternatives but complement each other in different scenarios, together constructing a comprehensive research framework for large model prediction.

The essential difference between the two core lines lies in whether a dedicated model is tailored for the prediction task: one path is to “borrow a boat to go to sea,” utilizing existing mature large language models for predictions; the other is to “build a ship for long voyages,” reconstructing a dedicated foundational model for predictions. Both paths advance simultaneously, adapting to diverse task requirements.

Directly calling large language models is the easiest entry point for large model prediction. Researchers convert various predictive tasks into simple natural language questions, providing the model with historical information, event background, and constraints, allowing it to judge future trends and output predictions directly. This method has a low barrier to entry, requiring no significant modifications to the model; it merely changes the application of existing tools, showing remarkable performance in news event analysis and business trend assessment. However, it falls short in high-precision numerical predictions required in fields like meteorology and finance due to the inherent limitations of large language models in numerical computation and potential biases in factual outputs.

Time series tokenization modeling represents a cross-domain “intelligent borrowing.” It cleverly introduces classic natural language processing ideas into time series data analysis, using techniques like discretization, scaling, and quantization to transform continuous time series data into token representations similar to words in language, and then trains using a language model-like architecture. The representative model, Chronos, maps time series to a fixed vocabulary, achieving probabilistic predictions and cross-dataset generalization, significantly reducing R&D costs. However, this convenience comes at a price, as the data transformation process inevitably leads to the loss of numerical details and quantization errors, akin to roughly polishing fine parts, which can affect prediction accuracy.

Building dedicated time series foundational models marks a shift from “borrowing momentum” to independent innovation in large model prediction research. Researchers no longer view time series simply as pseudo-text but design pre-training schemes and model architectures tailored to the essential laws of time series data and the core demands of prediction tasks. Google’s TimesFM, using a decoder architecture, demonstrates powerful zero-shot prediction capabilities; Lag-Llama, developed by multiple universities and research institutions in the U.S., focuses on probabilistic prediction and cross-domain generalization; and Moirai, developed by an American AI company, boldly attempts to adapt to more scenarios with a unified training approach. These models act like custom “armor” for prediction tasks, aligning closely with the characteristics of the tasks, achieving higher precision in numerical predictions, and becoming the preferred choice for high-precision prediction scenarios.

Reprogramming large language models and multimodal fusion provide a low-cost approach for large model prediction. Research related to Time-LLM confirms that it is unnecessary to retrain massive time series models with hundreds of billions of parameters; instead, reprogramming can precisely align time series with text prototypes, allowing the “frozen” large language models to participate in prediction tasks. This approach opens a feasible path for the general large model + specialized adaptation technical route, further promoting the deep joint modeling of text, numerical, and contextual knowledge, enabling predictions to integrate diverse and heterogeneous information like human thinking, better fitting the complex and variable demands of real-world prediction scenarios.

There is no absolute superiority among these four technical paths; they are like different keys that fit different locks. When a prediction task requires combining general knowledge and textual background for open trend judgments, the routes related to large language models act like a universal key, providing advantages; when high-precision numerical outputs and stable cross-domain generalization capabilities are pursued, dedicated time series foundational models become the custom key for precise matching. They support and complement each other under different R&D resource conditions and actual task requirements, jointly advancing large model predictive technology steadily forward.

Moving Towards Real-World Applications

In the research arena of large model predictive technology, international research started earlier and has developed a more systematic technical framework, delving deeper into foundational research and frontier exploration. Although domestic research began slightly later, it has rapidly caught up with strong momentum, forming unique advantages in scenario adaptation, open-source ecology, and practical application.

International academia’s research on large model prediction has evolved from text reasoning to multifaceted predictions. Early studies primarily focused on using large language models for text reasoning and event development judgments, akin to cultivating a small field; in the past two years, it has gradually broken boundaries, expanding into time series, spatiotemporal data, and even scientific predictions, entering a new phase of “expanding territory.” In the more complex field of scientific prediction, Microsoft’s ClimaX has pioneered the establishment of a foundational model framework for weather and climate tasks, while Aurora, also developed by Microsoft, extends the foundational model concept to the Earth system, capable of handling various prediction tasks such as weather, air quality, and wave forecasts, akin to equipping the Earth with an intelligent early warning system, showcasing the immense potential of scientific foundational models in complex system predictions.

Notably, the international academic community has maintained a rational and cautious attitude towards the predictive capabilities of large models. Research has shown that outstanding performance in standardized tests does not equate to reliability in real-world future event judgments—GPT-4, for instance, performed worse than the median human group in open-world prediction competitions. Addressing this core issue, international researchers have conducted competitive studies, retrieval enhancement research, and uncertainty detection studies, resulting in a distinctive characteristic of international research that emphasizes “model capability enhancement + prediction result verification + trustworthy mechanism construction,” laying a solid foundation for the practical application of technology.

Domestic research has leveraged the rapid development of general large models to achieve impressive late-stage catch-up, gradually forming a positive development pattern of rapid iteration of general large models, systematic review research, and steady advancement of application implementation. In the arena of general model ecosystem construction, various players have showcased their strengths: Qianwen 3 has established a complete system for multilingual support and inference efficiency optimization, akin to building a multilingual intelligent bridge; DeepSeek-V3 has achieved a technological breakthrough in high-performance open-source models, making core technologies more accessible; and Wenxin 4.5 has continuously refined multimodal fusion and engineering deployment, aligning closely with practical application needs. Although these general large models are not solely focused on prediction, they have built a solid capability foundation for domestic large model prediction research, enabling researchers to conduct more targeted studies while standing on the shoulders of giants.

In terms of application implementation, domestic efforts are actively exploring how to bring large model predictive technology out of the “ivory tower” and into real-world applications across various industries. Some studies have deeply integrated expert knowledge with large language models for strategic warning, achieving precise trend judgments and risk identification in complex situations; others have closely combined large models with meteorological monitoring data, attempting to enhance the accuracy and timeliness of short-term precipitation predictions. Although these studies are not entirely equivalent to pure numerical time series predictions, they signify that domestic large model predictive technology is transitioning from theoretical discussions to practical applications, beginning to explore technical paths that meet local needs and align with industry realities.

Overall, international research delves deeper into the development of dedicated foundational models for prediction and scientific prediction, akin to excavating a well-connected tunnel underground, forming a relatively complete technical system. In contrast, domestic research is more distinctive in adapting to Chinese scenarios, building low-cost open-source ecosystems, and implementing industry applications, akin to constructing high-rise buildings that fit local contexts above ground. With the continuous accumulation of high-quality time series data and industry-specific data domestically, as well as the gradual improvement of dedicated evaluation systems, there remains significant room for improvement in domestic foundational models aimed at prediction tasks, which will undoubtedly contribute unique and valuable Chinese wisdom to the global development of large model predictive technology.

Bridging the Gap from Powerful to Trustworthy

Compared to traditional predictive methods, large model predictive technology has achieved a profound transformation from “point calculations” to “comprehensive judgments,” evolving from cold, mechanical calculation tools into intelligent agents capable of understanding context, weighing factors, and providing rational judgments. This unique capability stems from its inherent core advantages, yet it is also like a growing star, steadily evolving towards becoming a trustworthy “future advisor”.

The core advantage of large model predictive technology is its innate exceptionalism, particularly evident in practical applications. Firstly, it possesses strong cross-task transfer capabilities. Traditional agricultural yield prediction models cannot be directly used for stock market trend analysis; switching fields requires starting from scratch. In contrast, large models, with their general representation capabilities from large-scale pre-training, can quickly adapt to different fields like agriculture, finance, and industry with few samples. Secondly, it has significant potential for handling complex dependencies. For instance, predicting river water levels during flood seasons is influenced by multiple factors such as rainfall, upstream flooding, and terrain, making it difficult for traditional models to capture these complex relationships, while time series foundational models can learn patterns within context, akin to having “piercing insight” into the connections behind the data. Thirdly, it excels in multi-source information fusion. Traditional meteorological predictions rely solely on numerical monitoring data, while large models can integrate satellite cloud images, meteorological text reports, geographic information, and other multi-source content, transforming predictions from a “narrow view” to a “panoramic observation.” Fourthly, it offers excellent prediction explanation and decision support capabilities. It can not only predict the trend of a specific stock but also explain the underlying industry policies, market supply and demand, and even provide risk control suggestions, becoming a professional intelligent partner for decision-makers.

Despite these significant advantages, large model predictive technology is not without flaws, and there remains a gap to bridge from the laboratory to real-world applications. Firstly, the model’s generative and inferential capabilities do not equate to actual predictive abilities. Some models may perform excellently in simulated meteorological prediction tests but often “fail” in real severe convective weather warnings, simply because the test answers are hidden within the training data, while real predictions require comprehensive judgments of unoccurred events—it’s easy to talk theoretically, but challenging to execute in practice. Secondly, retrieval enhancement addresses symptoms rather than root causes. While pairing models with information retrieval may improve prediction accuracy, it also indicates that models are relying solely on their memory of knowledge, akin to guarding an old library, making it difficult to keep pace with real-world changes; real-time access to the latest knowledge is crucial. Additionally, hallucinations and unstable facts present core obstacles, akin to hidden time bombs. Furthermore, constraints related to costs, data, and evaluation systems make large-scale applications challenging. Training high-precision models requires massive computational resources, leading to high R&D costs; in reality, time series data is fragmented and annotations are inconsistent—how can poor-quality materials produce high-quality products? Existing evaluation systems tend to emphasize numerical errors while downplaying factual stability, making many models appear excellent but difficult to implement.

Looking ahead, the development direction of large model predictive technology is clear and focused on “from powerful to trustworthy,” aiming to create a mature technical system that can stably serve real-world decision-making. Firstly, general large models will evolve into dedicated foundational models for predictions, demonstrating stronger competitiveness in high-precision demand scenarios like meteorology and finance. Secondly, tool enhancement will become an important direction, allowing models to autonomously call external tools like search and simulation, akin to equipping intelligent agents with a treasure trove to better handle complex scenarios. Thirdly, trustworthiness, controllability, and explainability will become research priorities; future prediction systems must not only be numerically accurate but also quantify risks and trace the basis of judgments, which is key for high-risk scenario implementation. Fourthly, accelerating low-cost deployment and industrialization will transform technology from being exclusive assets of a few institutions to becoming common tools across various industries as inference costs decrease and open-source ecosystems improve. Lastly, domestic research will focus on localized adaptation, creating dedicated models that integrate the Chinese context and local data, making large models more accurate, stable, and trustworthy in domestic financial risk control, government warning, and other scenarios.