LeForecast: Enterprise Hybrid Forecast by Time Series Intelligence

Demand is spiking in industrial fields for multidisciplinary forecasting, where a broad spectrum of sectors needs planning and forecasts to streamline intelligent business management, such as demand forecasting, product planning, inventory optimization, etc. Specifically, these tasks expecting intelligent approaches to learn from sequentially collected historical data and then foresee most possible trend, i.e. time series forecasting. Challenge of it lies in interpreting complex business contexts and the efficiency and generalisation of modelling. With aspirations of pre-trained foundational models for such purpose, given their remarkable success of large foundation model across legions of tasks, we disseminate \leforecast{}, an enterprise intelligence platform tailored for time series tasks. It integrates advanced interpretations of time series data and multi-source information, and a three-pillar modelling engine combining a large foundation model (Le-TSFM), multimodal model and hybrid model to derive insights, predict or infer futures, and then drive optimisation across multiple sectors in enterprise operations. The framework is composed by a model pool, model profiling module, and two different fusion approaches regarding original model architectures. Experimental results verify the efficiency of our trail fusion concepts: router-based fusion network and coordination of large and small models, resulting in high costs for redundant development and maintenance of models. This work reviews deployment of LeForecast and its performance in three industrial use cases. Our comprehensive experiments indicate that LeForecast is a profound and practical platform for efficient and competitive performance. And we do hope that this work can enlighten the research and grounding of time series techniques in accelerating enterprise.

@article{tan2025_2503.22747,
  title={ LeForecast: Enterprise Hybrid Forecast by Time Series Intelligence },
  author={ Zheng Tan and Yiwen Nie and Wenfa Wu and Guanyu Zhang and Yanze Liu and Xinyuan Tian and Kailin Gao and Mengya Liu and Qijiang Cheng and Haipeng Jiang and Yingzheng Ma and Wei Zheng and Yuci Zhu and Yuanyuan Sun and Xiangyu Lei and Xiyu Guan and Wanqing Huang and Shouming Liu and Xiangquan Meng and Pengzhan Qu and Chao Yang and Jiaxuan Fan and Yuan He and Hongsheng Qi and Yangzhou Du },
  journal={arXiv preprint arXiv:2503.22747},
  year={ 2025 }
}