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Bi-graph attention network for energy price forecasting via multiple time scale learning

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Abstract

Price forecasting of oil products and natural gas is of great interest due to their essential roles in modern industry and human lives. Existing studies on energy price forecasting are mostly concerned with individual energy markets, with less consideration of their interaction. This paper presents a bi-graph attention network (BiGAT) approach for energy price forecasting of oil products and natural gas, aiming at exploiting the price correlations of these energy sources for prediction. Specifically, we introduce the concordance graph and the causality graph into the BiGAT model to quantify the Kendall’s rank correlation and the convergence cross mapping causality of the energy prices. To facilitate training the BiGAT model with the energy price time series of multiple time scale nature, we employ the boosted Hodrick–Prescott (bHP) filter to decompose the price data into slow- and fast-varying parts for learning, respectively. As the original bHP filtering algorithm involves computing an inverse matrix of the data size, it is not in favor of using in expanding or streaming data scenarios directly. Here, we also devise an incremental bHP filtering algorithm that applies to data of arbitrary finite size in a recursive manner, requiring the computation of a third-order initial inverse matrix only. Experimental results on empirical data show that the forecasting accuracy of our model is significantly better than other considered models, and the proposed incremental algorithm effectively extends the application scenarios of the bHP filter.

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Data availability

The datasets generated during and analyzed during the current study are available in the U.S. Energy Information Administration repository, [https://www.eia.gov/.]

Abbreviations

ANNs:

Artificial neural networks

bHP filter:

Boosted Hodrick–Prescott filter

BiGAT:

Bi-graph attention network

BiGAT_p2:

BiGAT with \(p = 2\) operations of the HP filtering

BiGAT_p100:

BiGAT with \(p = 100\) operations of the HP filtering

BiGAT_p200:

BiGAT with \(p = 200\) operations of the HP filtering

BiGAT_p340:

BiGAT with \(p = 340\) operations of the HP filtering

CCM:

Convergence cross mapping

\({D_{\text{stat}}}\) :

Direction statistics

FFN:

Feed-forward neural network

GAT:

Graph attention network

GCNs:

Graph convolutional networks

GNN:

Graph neural network

GRA:

Gray relational analysis

GRD:

Gray relational degree

GRU:

Gated recurrent unit

HP filter:

Hodrick–Prescott filter

LSTM:

Long short-term memory

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

Max:

Maximum value

Min:

Minimum value

MSE:

Mean square error

Std:

Standard deviations

Var:

Variance

RNN:

Recurrent neural network

RMSE:

Root-mean-square error

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Authors and Affiliations

  1. College of Engineering, Peking University, Beijing, 100871, China

    Yuxia Liu, Wei Xiao & Tianguang Chu

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  1. Yuxia Liu
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  2. Wei Xiao
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Correspondence to Yuxia Liu.

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Liu, Y., Xiao, W. & Chu, T. Bi-graph attention network for energy price forecasting via multiple time scale learning. Neural Comput & Applic 35, 15943–15959 (2023). https://doi.org/10.1007/s00521-023-08583-0

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