LeForecast: Enterprise Hybrid Forecast by Time Series Intelligence
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arXiv
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| Format: | Preprint |
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2025
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| author | Tan, Zheng Nie, Yiwen Wu, Wenfa Zhang, Guanyu Liu, Yanze Tian, Xinyuan Gao, Kailin Liu, Mengya Cheng, Qijiang Jiang, Haipeng Ma, Yingzheng Zheng, Wei Zhu, Yuci Sun, Yuanyuan Lei, Xiangyu Guan, Xiyu Huang, Wanqing Liu, Shouming Meng, Xiangquan Qu, Pengzhan Yang, Chao Fan, Jiaxuan He, Yuan Qi, Hongsheng Du, Yangzhou |
| author_facet | Tan, Zheng Nie, Yiwen Wu, Wenfa Zhang, Guanyu Liu, Yanze Tian, Xinyuan Gao, Kailin Liu, Mengya Cheng, Qijiang Jiang, Haipeng Ma, Yingzheng Zheng, Wei Zhu, Yuci Sun, Yuanyuan Lei, Xiangyu Guan, Xiyu Huang, Wanqing Liu, Shouming Meng, Xiangquan Qu, Pengzhan Yang, Chao Fan, Jiaxuan He, Yuan Qi, Hongsheng Du, Yangzhou |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22747 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LeForecast: Enterprise Hybrid Forecast by Time Series Intelligence Tan, Zheng Nie, Yiwen Wu, Wenfa Zhang, Guanyu Liu, Yanze Tian, Xinyuan Gao, Kailin Liu, Mengya Cheng, Qijiang Jiang, Haipeng Ma, Yingzheng Zheng, Wei Zhu, Yuci Sun, Yuanyuan Lei, Xiangyu Guan, Xiyu Huang, Wanqing Liu, Shouming Meng, Xiangquan Qu, Pengzhan Yang, Chao Fan, Jiaxuan He, Yuan Qi, Hongsheng Du, Yangzhou Machine Learning Artificial Intelligence Emerging Technologies 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. |
| title | LeForecast: Enterprise Hybrid Forecast by Time Series Intelligence |
| topic | Machine Learning Artificial Intelligence Emerging Technologies |
| url | https://arxiv.org/abs/2503.22747 |