120 citations · 279 across the 18 of their papers we have counts for
21 papers
Probabilistic forecasting via post-processing prediction errors: In- or out-of-sample?
Piotr Zaborowski, Arkadiusz Lipiecki, Fotios Petropoulos +1
Many forecasting systems produce point forecasts even when decisions require information about uncertainty. We investigate whether post-processing methods can systematically improv…
Multi-objective probabilistic forecast combination for inventory demand
Shengjie Wang, Yanfei Kang, Evangelos Spiliotis +1
Probabilistic forecasts are essential for inventory management, where decisions depend on the full distribution of future demand. While probabilistic forecast combination is widely…
Operations & Supply Chain Management: Principles and Practice
Fotios Petropoulos, Henk Akkermans, O. Zeynep Aksin +90
Operations and Supply Chain Management (OSCM) has continually evolved, incorporating a broad array of strategies, frameworks, and technologies to address complex challenges across…
Scaleable Dynamic Forecast Reconciliation
Ross Hollyman, Fotios Petropoulos, Michael E. Tipping
We introduce a dynamic approach to probabilistic forecast reconciliation at scale. Our model differs from the existing literature in this area in several important ways. Firstly we…
The M6 forecasting competition: Bridging the gap between forecasting and investment decisions
Spyros Makridakis, Evangelos Spiliotis, Ross Hollyman +3
The M6 forecasting competition, the sixth in the Makridakis' competition sequence, is focused on financial forecasting. A key objective of the M6 competition was to contribute to t…
Probabilistic Forecast-based Portfolio Optimization of Electricity Demand at Low Aggregation Levels
Jungyeon Park, Estêvão Alvarenga, Jooyoung Jeon +4
In the effort to achieve carbon neutrality through a decentralized electricity market, accurate short-term load forecasting at low aggregation levels has become increasingly crucia…
Combining Probabilistic Forecasts of Intermittent Demand
Shengjie Wang, Yanfei Kang, Fotios Petropoulos
In recent decades, new methods and approaches have been developed for forecasting intermittent demand series. However, the majority of research has focused on point forecasting, wi…
Operational Research: Methods and Applications
Fotios Petropoulos, Gilbert Laporte, Emel Aktas +79
Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This…
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…
Feature-based intermittent demand forecast combinations: bias, accuracy and inventory implications
Li Li, Yanfei Kang, Fotios Petropoulos +1
Intermittent demand forecasting is a ubiquitous and challenging problem in production systems and supply chain management. In recent years, there has been a growing focus on develo…
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…
Wielding Occam's razor: Fast and frugal retail forecasting
Fotios Petropoulos, Yael Grushka-Cockayne, Enno Siemsen +1
The algorithms available for retail forecasting have increased in complexity. Newer methods, such as machine learning, are inherently complex. The more traditional families of fore…
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…
Improving forecasting by subsampling seasonal time series
Xixi Li, Fotios Petropoulos, Yanfei Kang
Time series forecasting plays an increasingly important role in modern business decisions. In today's data-rich environment, people often aim to choose the optimal forecasting mode…
Forecasting: theory and practice
Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos +77
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organ…
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…
The uncertainty estimation of feature-based forecast combinations
Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos +1
Forecasting is an indispensable element of operational research (OR) and an important aid to planning. The accurate estimation of the forecast uncertainty facilitates several opera…
Reconciliation of probabilistic forecasts with an application to wind power
Jooyoung Jeon, Anastasios Panagiotelis, Fotios Petropoulos
New methods are proposed for adjusting probabilistic forecasts to ensure coherence with the aggregation constraints inherent in temporal hierarchies. The different approaches neste…
The Optimised Theta Method
José Augusto Fioruci, Tiago Ribeiro Pellegrini, Francisco Louzada +1
Accurate and robust forecasting methods for univariate time series are very important when the objective is to produce estimates for a large number of time series. In this context,…