33 citations · 93 across the 10 of their papers we have counts for
13 papers
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…
A Trustworthy By Design Classification Model for Building Energy Retrofit Decision Support
Panagiota Rempi, Sotiris Pelekis, Alexandros Menelaos Tzortzis +4
Improving energy efficiency in residential buildings is critical to combating climate change and reducing greenhouse gas emissions. Retrofitting existing buildings, which contribut…
Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time series
Alexandros-Menelaos Tzortzis, Sotiris Pelekis, Evangelos Spiliotis +3
Short-term load forecasting (STLF) is crucial for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand…
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…
A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers
Sotiris Pelekis, Ioannis-Konstantinos Seisopoulos, Evangelos Spiliotis +4
Short-term load forecasting (STLF) is vital for the effective and economic operation of power grids and energy markets. However, the non-linearity and non-stationarity of electrici…
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…
Exploring the representativeness of the M5 competition data
Evangelos Theodorou, Shengjie Wang, Yanfei Kang +3
The main objective of the M5 competition, which focused on forecasting the hierarchical unit sales of Walmart, was to evaluate the accuracy and uncertainty of forecasting methods i…
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…
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…
Model selection in reconciling hierarchical time series
Mahdi Abolghasemi, Rob J Hyndman, Evangelos Spiliotis +1
Model selection has been proven an effective strategy for improving accuracy in time series forecasting applications. However, when dealing with hierarchical time series, apart fro…
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…