Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity
<|reference_start|>Large Scale Hierarchical Industrial Demand Time-Series Forecasting incorporating Sparsity: Hierarchical time-series forecasting (HTSF) is an important problem for many real-world business applications where the goal is to simultaneously forecast multiple time-series that are related to each other via a hierarchical relation. Recent works, however, do not address two important challenges that are typically observed in many demand forecasting applications at large companies. First, many time-series at lower levels of the hierarchy have high sparsity i.e., they have a significant number of zeros. Most HTSF methods do not address this varying sparsity across the hierarchy. Further, they do not scale well to the large size of the real-world hierarchy typically unseen in benchmarks used in literature. We resolve both these challenges by proposing HAILS, a novel probabilistic hierarchical model that enables accurate and calibrated probabilistic forecasts across the hierarchy by adaptively modeling sparse and dense time-series with different distributional assumptions and reconciling them to adhere to hierarchical constraints. We show the scalability and effectiveness of our methods by evaluating them against real-world demand forecasting datasets. We deploy HAILS at a large chemical manufacturing company for a product demand forecasting application with over ten thousand products and observe a significant 8.5\% improvement in forecast accuracy and 23% better improvement for sparse time-series. The enhanced accuracy and scalability make HAILS a valuable tool for improved business planning and customer experience.<|reference_end|>
@article{kamarthi2024large,
    title={Large Scale Hierarchical Industrial Demand Time-Series Forecasting
  incorporating Sparsity},
    author={Harshavardhan Kamarthi, Aditya B. Sasanur, Xinjie Tong, Xingyu Zhou,
  James Peters, Joe Czyzyk, B. Aditya Prakash},
    journal={arXiv preprint arXiv:2407.02657},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02657},
    primaryClass={cs.LG stat.ME}
}