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Found 7 papers, 3 papers with code
Probabilistic Forecast-based Portfolio Optimization of Electricity Demand at Low Aggregation Levels
Using probabilistic load forecasts produced by either ARMA-GARCH models or kernel density estimation (KDE), we propose three approaches to creating a portfolio of residential households' demand: Forecast Validated, Seasonal Residual, and Seasonal Similarity.
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
Forecast with Forecasts: Diversity Matters
In this work, we suggest a change of focus from the historical data to the produced forecasts to extract features.
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
Que será será? The uncertainty estimation of feature-based time series forecasts
In the training part, we use a collection of time series to train a model to explore how time series features affect the interval forecasting accuracy of different forecasting methods, which makes our proposed framework interpretable in terms of the contribution of each feature to the models' uncertainty prediction.
Methodology Applications Computation
The Optimised Theta Method
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.
Methodology 62M10
