Forecasting with temporal hierarchies
By George Athanasopoulos, Rob J Hyndman, Nikolaos Kourentzes, Fotios Petropoulos. 2017
268 citations recorded by the source corpus.
Subfields: Management Science and Operations Research, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Science and Operations Research
- Rob J Hyndman
- Fotios Petropoulos
- Konstantinos Nikolopoulos
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Spyros Makridakis
- Christoph Bergmeir
- Evangelos Spiliotis
In Signal Processing
- Rob J Hyndman
- Bernie Wang
- Vassilios Assimakopoulos
- Fotios Petropoulos
- Evangelos Spiliotis
- Spyros Makridakis
- Tim Januschowski
- Konstantinos Nikolopoulos
Citations in the corpus
- Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2020, background): “Dedicated work exists for aggregation along the time dimension [8, 147, 178].”
- Forecasting: theory and practice (2020, methodology): “Barrow and Crone (2016b) developed cross-validation and aggregating (Crogging) using cross-validation to generate and average multiple forecasts, while more recently, combinations of forecasts generated from multiple temporal levels has become popular (Kourentzes et al., 2014; Athanasopoulos et al.,”
- Forecasting hierarchical and grouped time series through trace minimization (2015, methodology): “This estimator was proposed by Athanasopoulos et al. (2015) for temporal hierarchies.”
- Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization (2018, unknown): “This specification is proposed by Athanasopoulos et al. (2017) for temporal hierarchies and assumes that each of the bottom-level base forecast errors has a variance k h and are uncorrelated between nodes.”
- Forecasting in social settings: The state of the art (2020, background): “Current approaches to forecast combinations include, among others, combinations based on information criteria (Kolassa, 2011), the use of multiple temporal aggregation levels (Andrawis et al., 2011; Kourentzes et al., 2014; Athanasopoulos et al., 2017), bootstrapping for time series forecasting (”
- Tales from tails: On the empirical distributions of forecasting errors and their implication to risk (2019, methodology): “More recently, Athanasopoulos et al. (2017) provided an understanding as to why multiple temporal aggregation leads to improved forecasts and Petropoulos et al. (2018) explained why does bagging for time series forecasting works.”