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Forecasting energy consumption in Sweden

Ferdinand-Dreyfus, Jonathan LU (2022) NEKH03 20221
Department of Economics
Abstract
Machine learning has acquired a lot of attention in the economic forecasting literature in recent years. In this thesis we forecast Swedish energy consumption and compare the forecasting performance of a machine learning technique with that of more traditional time series models. In fact, the LSTM neural network is compared with ARIMA and VAR forecasts. We conclude that in our setting, while these newer techniques perform well under some conditions and are able to outperform the ARIMA forecast, they are not found to outperform the VAR model which remains the best modelling choice among those considered here.
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author
Ferdinand-Dreyfus, Jonathan LU
supervisor
organization
course
NEKH03 20221
year
type
M2 - Bachelor Degree
subject
keywords
ARIMA, VAR, LSTM, Energy consumption, Machine Learning
language
English
id
9085730
date added to LUP
2022-10-10 09:12:03
date last changed
2022-10-10 09:12:03
@misc{9085730,
  abstract     = {{Machine learning has acquired a lot of attention in the economic forecasting literature in recent years. In this thesis we forecast Swedish energy consumption and compare the forecasting performance of a machine learning technique with that of more traditional time series models. In fact, the LSTM neural network is compared with ARIMA and VAR forecasts. We conclude that in our setting, while these newer techniques perform well under some conditions and are able to outperform the ARIMA forecast, they are not found to outperform the VAR model which remains the best modelling choice among those considered here.}},
  author       = {{Ferdinand-Dreyfus, Jonathan}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Forecasting energy consumption in Sweden}},
  year         = {{2022}},
}