7 citations · 7 across the 3 of their papers we have counts for
9 papers
Hierarchies Everywhere -- Managing & Measuring Uncertainty in Hierarchical Time Series
Ross Hollyman, Fotios Petropoulos, Michael E. Tipping
We examine the problem of making reconciled forecasts of large collections of related time series through a behavioural/Bayesian lens. Our approach explicitly acknowledges and expl…
Model combinations through revised base-rates
Fotios Petropoulos, Evangelos Spiliotis, Anastasios Panagiotelis
Standard selection criteria for forecasting models focus on information that is calculated for each series independently, disregarding the general tendencies and performances of th…
The future of forecasting competitions: Design attributes and principles
Spyros Makridakis, Chris Fry, Fotios Petropoulos +1
Forecasting competitions are the equivalent of laboratory experimentation widely used in physical and life sciences. They provide useful, objective information to improve the theor…
Forecast with Forecasts: Diversity Matters
Yanfei Kang, Wei Cao, Fotios Petropoulos +1
Forecast combinations have been widely applied in the last few decades to improve forecasting. Estimating optimal weights that can outperform simple averages is not always an easy…
Hierarchical forecast reconciliation with machine learning
Evangelos Spiliotis, Mahdi Abolghasemi, Rob J Hyndman +2
Hierarchical forecasting methods have been widely used to support aligned decision-making by providing coherent forecasts at different aggregation levels. Traditional hierarchical…
Déjà vu: A data-centric forecasting approach through time series cross-similarity
Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos +3
Accurate forecasts are vital for supporting the decisions of modern companies. Forecasters typically select the most appropriate statistical model for each time series. However, st…