The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Forecasting at Scale
Open accessAbstract
Forecasting is a common data science task that helps organizations with capacity planning, goal setting, and anomaly detection. Despite its importance, there are serious challenges associated with producing reliable and high-quality forecasts—especially when there are a variety of time series and analysts with expertise in time series modeling are relatively rare. To address these challenges, we describe a practical approach to forecasting “at scale” that combines configurable models with analyst-in-the-loop performance analysis. We propose a modular regression model with interpretable parameters that can be intuitively adjusted by analysts with domain knowledge about the time series. We describe performance analyses to compare and evaluate forecasting procedures, and automatically flag forecasts for manual review and adjustment. Tools that help analysts to use their expertise most effectively enable reliable, practical forecasting of business time series.
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Cites
Stan : A Probabilistic Programming Language
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017) · Journal of Statistical Software · doi:10.18637/jss.v076.i01 · Cited by 7,754 · cited 1×
“…sum of each of these components, plus noise: When the seasonality and holiday features for each observation are combined into a matrix X and the changepoint indicators a ( t ) in a matrix A , the entire model can be expressed in a few lines of Stan code [Carpenter et al., 2016], given in Listing 1.”
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Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., & Zhang, W. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17325 · Cited by 7,752
Fake news on Twitter during the 2016 U.S. presidential election
Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B., & Lazer, D. (2019) · Science · doi:10.1126/science.aau2706 · Cited by 1,803
Seasonal trends in COVID-19 cases, hospitalizations, and mortality in the United States and Europe
Wiemken, T. L., Khan, F., Puzniak, L., Yang, W., Simmering, J., Polgreen, P., Nguyen, J. L., Jodar, L., & McLaughlin, J. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-31057-1 · Cited by 138
Intravitreal injections: past trends and future projections within a UK tertiary hospital
Chopra, R., Preston, G. C., Keenan, T. D. L., Mulholland, P., Patel, P. J., Balaskas, K., Hamilton, R. D., & Keane, P. A. (2022) · Eye · doi:10.1038/s41433-021-01646-3 · Cited by 83
AutoAI-TS: AutoAI for Time Series Forecasting
Shah, S. Y., Patel, D., Vu, L., Dang, X.-H., Chen, B., Kirchner, P., Samulowitz, H., Wood, D., Bramble, G., Gifford, W. M., et al. (2021) · Proceedings of the 2021 International Conference on Management of Data · doi:10.1145/3448016.3457557 · Cited by 39
Ren, J., Wang, A., Li, H., Yue, X., & Meng, L. (2023) · Sensors · doi:10.3390/s23146547 · Cited by 34
Aslam, S., Rasool, A., Li, X., & Wu, H. (2025) · Interdisciplinary Sciences: Computational Life Sciences · doi:10.1007/s12539-024-00675-2 · Cited by 23
Kalfarisi, R., Chew, A., Cai, J., Xue, M., Pok, J., & Wu, Z. Y. (2022) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2022.103287 · Cited by 10
Sequential Monte Carlo Learning for Time Series Structure Discovery
Saad, F. A., Patton, B. J., Hoffman, M. D., Saurous, R. A., & Mansinghka, V. K. (2023) · arXiv · doi:10.48550/arxiv.2307.09607 · Cited by 3
Metadata
- Authors
- Taylor, S. J., & Letham, B.
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- Published
- 2018-01-02
- Volume
- 72
- Issue
- 1
- Pages
- 37-45
- ISSN
- 0003-1305 (ISSN-L)
- 1537-2731
- OpenAlex
- W2747599906
- Cited by
- 2,737
- References
- 1
- FWCI
- 69.64
- Type
- journal article
- Language
- en
- Open access
- gold
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