Fast Forecast Reconciliation Using Linear Models
Author:
Affiliation:
1. Institute of Service Science, National Tsing Hua University;
2. Department of Econometrics & Business Statistics, Monash University
Publisher
Informa UK Limited
Subject
Statistics, Probability and Uncertainty,Discrete Mathematics and Combinatorics,Statistics and Probability
Link
https://www.tandfonline.com/doi/pdf/10.1080/10618600.2021.1939038
Reference20 articles.
1. Akaike, H. (1998). Information theory and an extension of the maximum likelihood principle. In Selected Papers of Hirotugu Akaike, pp. 199–213. Springer Series in Statistics (Perspectives in Statistics). Springer.
2. Ashouri, M., G. Shmueli, and C.Y. Sin (2019). Tree-based methods for clustering time series using domain-relevant attributes. Journal of Business Analytics 2 (1), 1–23.
3. Athanasopoulos, G., R. A. Ahmed, and R. J. Hyndman (2009). Hierarchical forecasts for australian domestic tourism. International Journal of Forecasting 25 (1), 146–166.
4. Bunch, J. R. and D. J. Rose (2014). Sparse matrix computations. Academic Press.
5. Christensen, R. (2020). Plane answers to complex questions: the theory of linear models (5th ed.). Springer.
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