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Anastasios Kaltsounis, Evangelos Spiliotis and Vassilios Assimakopoulos
We present a machine learning approach for applying (multiple) temporal aggregation in time series forecasting settings. The method utilizes a classification model that can be used to either select the most appropriate temporal aggregation level for prod...
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Elissaios Sarmas, Evangelos Spiliotis, Nikos Dimitropoulos, Vangelis Marinakis and Haris Doukas
Energy efficiency financing is considered among the top priorities in the energy sector among several stakeholders. In this context, accurately estimating the energy savings achieved by energy efficiency actions before being approved and implemented is o...
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Evangelos Spiliotis, Fotios Petropoulos and Vassilios Assimakopoulos
Forecasters have been using various criteria to select the most appropriate model from a pool of candidate models. This includes measurements on the in-sample accuracy of the models, information criteria, and cross-validation, among others. Although the ...
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Fotios Petropoulos and Evangelos Spiliotis
Forecasting is a challenging task that typically requires making assumptions about the observed data but also the future conditions. Inevitably, any forecasting process will result in some degree of inaccuracy. The forecasting performance will further de...
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