Increasing global energy demands and decreasing stocks of fossil fuels have led to a resurgence of research into energy forecasting. Artificial intelligence, explicitly time series forecasting holds great potential to improve predictions of cost and demand with many lucrative applications across several fields. Many factors influence prices on a global scale, from socio-economic factors to distribution, availability, and international policy. Also, various factors need to be considered in order to make an accurate forecast. By analyzing the current literature, a gap for improvements within this domain exists. Therefore, this work suggests and explores the potential of multi-headed long short-term memory models for gasoline price forecasting, since this issue was not tackled with multi-headed models before. Additionally, since the computational requirements for such models are relatively high, work focuses on lightweight approaches that consist of a relatively low number of neurons per layer, trained in a small number of epochs. However, as algorithm performance can be heavily dependent on appropriate hyper-parameter selections, a modified variant of the particle swarm optimization algorithm is also set forth to help in optimizing the model’s architecture and training parameters. A comparative analysis is conducted using energy data collected from multiple public sources between several contemporary optimizers. The outcomes are put through a meticulous statistical validation to ascertain the significance of the findings. The best-constructed models attained a mean square error of just 0.044025 with an R-squared of 0.911797, suggesting potential for real-world use.

中文翻译：

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粒子群优化：用于燃料价格预测的调整多头长短期记忆网络方法


全球能源需求的增加和化石燃料库存的减少导致了能源预测研究的复兴。人工智能，明确的时间序列预测，具有改进成本和需求预测的巨大潜力，在多个领域有许多有利可图的应用。许多因素在全球范围内影响价格，从社会经济因素到分销、可用性和国际政策。此外，为了做出准确的预测，需要考虑各种因素。通过分析当前的文献，该领域存在改进的差距。因此，这项工作提出并探讨了多头长短期记忆模型在汽油价格预测中的潜力，因为这个问题以前没有用多头模型来解决。此外，由于此类模型的计算要求相对较高，因此工作集中在轻量级方法上，这些方法由每层相对较少的神经元组成，并在少量 epoch 中训练。然而，由于算法性能在很大程度上取决于适当的超参数选择，因此还提出了粒子群优化算法的修改变体，以帮助优化模型的架构和训练参数。使用从多个公共来源收集的能源数据在几个当代优化者之间进行比较分析。结果经过细致的统计验证，以确定结果的重要性。最佳构建的模型仅获得 0.044025 的均方误差，R 平方为 0.911797，这表明有可能在现实世界中使用。