Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering

We propose a novel approach to cluster hierarchical time series (HTS) for efficient forecasting and data analysis. Inspired by a practically important but unstudied problem, we found that leveraging local information when clustering HTS will lead to better performance. The clustering procedure we proposed can cope with massive HTS with arbitrary lengths and structures. This method, besides providing better insights from the data, can also be used to accelerate the forecasts for a large number of HTS. Each time series is first assigned the forecast from its cluster representative, which can be considered as a ``shrinkage prior’’ for the set of time series it represents. Then this base forecast can be quickly fine-tuned to adjust to the specifics of that time series. We empirically show that our method substantially improves performance for large-scale clustering and forecasting tasks involving much HTS.

Arxiv Conference

Recommended citation:

@article{han2022efficient,
  title={Efficient Forecasting of Large Scale Hierarchical Time Series via Multilevel Clustering},
  author={Han, Xing and Ren, Tongzheng and Hu, Jing and Ghosh, Joydeep and Ho, Nhat},
  journal={arXiv preprint arXiv:2205.14104},
  year={2022}
}