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Found 7 papers, 2 papers with code
Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time series
Short-term load forecasting (STLF) is crucial for the daily operation of power grids.
A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers
Short-term load forecasting (STLF) is vital for the effective and economic operation of power grids and energy markets.
Exploring the representativeness of the M5 competition data
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 in the field in order to identify best practices and highlight their practical implications.
The future of forecasting competitions: Design attributes and principles
Forecasting competitions are the equivalent of laboratory experimentation widely used in physical and life sciences.
Applications
Model selection in reconciling hierarchical time series
However, when dealing with hierarchical time series, apart from selecting the most appropriate forecasting model, forecasters have also to select a suitable method for reconciling the base forecasts produced for each series to make sure they are coherent.
Hierarchical forecast reconciliation with machine learning
First, the proposed method allows for a non-linear combination of the base forecasts, thus being more general than the linear approaches.
Déjà vu: forecasting with similarity
Instead of extrapolating, the future paths of the similar reference series are aggregated and serve as the basis for the forecasts of the target series.
Methodology Applications
