Authors :
Muhammad Bilyaminu Abdullahi; Kabiru Ibrahim Musa; Abdulsalam Ya’u Gital; Auwal Nata`ala; Abubakar Umar Lawan; Ismail Zahraddeen Yakubu
Volume/Issue :
Volume 8 - 2023, Issue 12 - December
Google Scholar :
http://tinyurl.com/bddmxhua
Scribd :
http://tinyurl.com/797f64sz
DOI :
https://doi.org/10.5281/zenodo.10453152
Abstract :
As one-third of the world's energy
consumption, crude oil is vital to the global economy,
yet because of its volatility and complexity, it is still
difficult to estimate its price.Although machine learning
models might enhance forecasts, they are not
impervious to unanticipated shocks, geopolitical events,
and uncertainty in the world economy. Few studies have
employed hybrid models to increase prediction
accuracy, despite a large body of research on machine-
learning models' potential to improve forecasting. The
current approach for predicting petroleum prices
forecasts for a short time horizon (10 days) by ignoring
outside data that could enhance the prediction
performance. Though useful for many in the oil and gas
sector, short-term petroleum price forecasting has some
limitations and challenges of its own, including limited
accuracy, volatility and uncertainty, and a potential
inability to fully account for the unpredictable effects of
government policies on petroleum prices. In the oil and
gas sector, medium- to long-term predictions may offer
more consistent and trustworthy direction for strategic
planning. This degree of selective attention is also
lacking in current skip connection-based forecasting
algorithms. When the network is producing predictions,
attention mechanisms enable it to choose focus on
distinct segments of the input sequence. As a result, the
skip connection increases the model's computational
complexity, necessitates a large amount of memory,
adds noise and redundancy, and needs to be carefully
designed and tuned to fit the network architecture and
data domain. In order to increase the accuracy of
petroleum price predictions, this study suggests
combining the benefits of long short-term memory
(LSTM), CNN, and attention connection. The proposed
model outperformed the classical skip base CNN-LSTM
algorithm, which came in second place with an MAE
and RMSE of 0.0231 and 0.0297, and skip base CNN-
GRU, which achieved the highest MAE and RMSE of
0.0236 and 0.0318, respectively, according to
experimental results on MATLAB 2022a. The proposed
model also achieved the lowest MAE and RMSE values
of 0.0175 and 0.0199.
Keywords :
Attention; Deep Learning; Convolutional Neural Network; Long Short-Term Memory; Machine Learning; Petroleum Price and Forecasting.
As one-third of the world's energy
consumption, crude oil is vital to the global economy,
yet because of its volatility and complexity, it is still
difficult to estimate its price.Although machine learning
models might enhance forecasts, they are not
impervious to unanticipated shocks, geopolitical events,
and uncertainty in the world economy. Few studies have
employed hybrid models to increase prediction
accuracy, despite a large body of research on machine-
learning models' potential to improve forecasting. The
current approach for predicting petroleum prices
forecasts for a short time horizon (10 days) by ignoring
outside data that could enhance the prediction
performance. Though useful for many in the oil and gas
sector, short-term petroleum price forecasting has some
limitations and challenges of its own, including limited
accuracy, volatility and uncertainty, and a potential
inability to fully account for the unpredictable effects of
government policies on petroleum prices. In the oil and
gas sector, medium- to long-term predictions may offer
more consistent and trustworthy direction for strategic
planning. This degree of selective attention is also
lacking in current skip connection-based forecasting
algorithms. When the network is producing predictions,
attention mechanisms enable it to choose focus on
distinct segments of the input sequence. As a result, the
skip connection increases the model's computational
complexity, necessitates a large amount of memory,
adds noise and redundancy, and needs to be carefully
designed and tuned to fit the network architecture and
data domain. In order to increase the accuracy of
petroleum price predictions, this study suggests
combining the benefits of long short-term memory
(LSTM), CNN, and attention connection. The proposed
model outperformed the classical skip base CNN-LSTM
algorithm, which came in second place with an MAE
and RMSE of 0.0231 and 0.0297, and skip base CNN-
GRU, which achieved the highest MAE and RMSE of
0.0236 and 0.0318, respectively, according to
experimental results on MATLAB 2022a. The proposed
model also achieved the lowest MAE and RMSE values
of 0.0175 and 0.0199.
Keywords :
Attention; Deep Learning; Convolutional Neural Network; Long Short-Term Memory; Machine Learning; Petroleum Price and Forecasting.
