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A Mixed Time-Series & Machine Learning Approach for Price Forecasting in the Swedish Ancillary Market

Arredondo, Daniel LU (2023) DABN01 20231
Department of Economics
Department of Statistics
Abstract
This study aims to forecast the Swedish FCR-D Down A2 market prices through a hybrid model combining a volatility model and a machine learning approach, and compares its performance with a standalone machine learning model. We further examine the impact of different lag orders (1-Hr vs. 24-Hr) on volatility estimates and forecast performance. Evaluation metrics include root of mean squared error (RMSE) and mean absolute error (MAE). Results suggest that the hybrid model incorporating day-to-day volatility changes effectively predicts market price spikes, providing a competitive edge for energy traders. However, this model falls short during docile market periods, indicating a need for supplementary models. The study concludes that the... (More)
This study aims to forecast the Swedish FCR-D Down A2 market prices through a hybrid model combining a volatility model and a machine learning approach, and compares its performance with a standalone machine learning model. We further examine the impact of different lag orders (1-Hr vs. 24-Hr) on volatility estimates and forecast performance. Evaluation metrics include root of mean squared error (RMSE) and mean absolute error (MAE). Results suggest that the hybrid model incorporating day-to-day volatility changes effectively predicts market price spikes, providing a competitive edge for energy traders. However, this model falls short during docile market periods, indicating a need for supplementary models. The study concludes that the current hybrid model does not yield significant forecast performance increases compared to simpler machine learning models, prompting further investigation into the optimization of such hybrid models for effective market price forecasting. (Less)
Please use this url to cite or link to this publication:
author
Arredondo, Daniel LU
supervisor
organization
course
DABN01 20231
year
type
H1 - Master's Degree (One Year)
subject
keywords
Hybrid model, Volatility model, Machine learning model, Price spike prediction, Energy trading, Time series forecasting, Ancillary service market
language
English
additional info
Thesis completed with aid from Modity Energy Trading AB.
id
9119549
date added to LUP
2023-11-21 12:53:25
date last changed
2023-11-21 12:53:25
@misc{9119549,
  abstract     = {{This study aims to forecast the Swedish FCR-D Down A2 market prices through a hybrid model combining a volatility model and a machine learning approach, and compares its performance with a standalone machine learning model. We further examine the impact of different lag orders (1-Hr vs. 24-Hr) on volatility estimates and forecast performance. Evaluation metrics include root of mean squared error (RMSE) and mean absolute error (MAE). Results suggest that the hybrid model incorporating day-to-day volatility changes effectively predicts market price spikes, providing a competitive edge for energy traders. However, this model falls short during docile market periods, indicating a need for supplementary models. The study concludes that the current hybrid model does not yield significant forecast performance increases compared to simpler machine learning models, prompting further investigation into the optimization of such hybrid models for effective market price forecasting.}},
  author       = {{Arredondo, Daniel}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{A Mixed Time-Series & Machine Learning Approach for Price Forecasting in the Swedish Ancillary Market}},
  year         = {{2023}},
}