Data augmentation for time series regression: Applying transformations, autoencoders and adversarial networks to electricity price forecasting

Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark

(2021) Jesus Lago et al.   APPLIED ENERGY

An effective Two-Stage Electricity Price forecasting scheme

(2021) Wei Shi et al.   ELECTRIC POWER SYSTEMS RESEARCH

An adaptive hybrid model for short term electricity price forecasting

(2019) Jinliang Zhang et al.   APPLIED ENERGY

Introducing Technical Indicators to Electricity Price Forecasting: A Feature Engineering Study for Linear, Ensemble, and Deep Machine Learning Models

(2019) Sumeyra Demir et al.   Applied Sciences-Basel

Forecasting day-ahead electricity prices in Europe: The importance of considering market integration

(2018) Jesus Lago et al.   APPLIED ENERGY

Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms

(2018) Jesus Lago et al.   APPLIED ENERGY

Electricity Price Forecasting Using Recurrent Neural Networks

(2018) Umut Ugurlu et al.   Energies

Perceptual Adversarial Networks for Image-to-Image Transformation

(2018) Chaoyue Wang et al.   IEEE TRANSACTIONS ON IMAGE PROCESSING

Efficient Forecasting of Electricity Spot Prices with Expert and LASSO Models

(2018) Bartosz Uniejewski et al.   Energies

GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification

(2018) Maayan Frid-Adar et al.   NEUROCOMPUTING

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

(2016) Bartosz Uniejewski et al.   Energies