LeForecast: Enterprise Hybrid Forecast by Time Series Intelligence
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Abstract (arXiv)
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.
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References · 21
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- 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
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- 2023FITS: Modeling Time Series with 10k Parameterscited by 185
- 2023Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Modelscited by 141
9 more
- 2024Knowledge Fusion of Large Language Modelscited by 110
- 2023Deep Model Fusion: A Surveycited by 89
- 2024Merging Multi-Task Models via Weight-Ensembling Mixture of Expertscited by 85
- 2023Fusing Models with Complementary Expertisecited by 45
- 2023Small Models are Valuable Plug-ins for Large Language Modelscited by 42
- 2024Empowering Time Series Analysis with Foundation Models: A Comprehensive Surveycited by 23
- 2024Improving Large Models with Small models: Lower Costs and Better Performancecited by 14
- 2023Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shiftscited by 5
- 2024Dataset-Driven Channel Masks in Transformers for Multivariate Time Seriescited by 3
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