## 1 Introduction  
Predicting commodities prices in the futures market has always been a classic and challenging problem that has an impact on many countries and even the whole world [1]. Economists and computer scientists are interested in predicting futures commodity prices in economic market research [2, 3]. Known for its extensive hedging capabilities and stable value, gold holds a prominent place in the investment and financial markets. The development and export of the natural gas industry play a key role in the economic growth of many countries, and natural gas exports can generate significant foreign exchange earnings for the country. Forecasts suggest that natural gas is poised to supersede coal and oil as the predominant fossil fuel post-2030 in terms of its share of the world's primary energy consumption [4, 5]. The world economy, geopolitics, inflation, and other factors all have an impact on commodities futures. Therefore, when predicting the value of these futures commodities, traditional methods require careful attention to market dynamics and relevant macroeconomic data. At the same time, researchers also face the challenge of improving the accuracy of futures commodity price volatility prediction models, as improving predictive performance is crucial for related companies and stakeholders [6]. For the investor to grasp futures commodity prices in advance of the day before the opening of the market so that they can decide whether to continue to buy or sell, thereby affecting the return on the capital, it is crucial to design an accurate forecasting model to predict commodity price trends. During exploration and exploitation, the ABC optimization algorithm can easily slip into local optima due to its poor convergence speed [7,8]. Simultaneously, optimizing neural network hyperparameters for the faster training of high-precision prediction models is another complicated issue. Hyperparameter adjustments take a large amount of time in traditional methods, and even minor alterations can exert a huge impact on the outcome of the prediction [9,10]. In this paper, we propose a novel approach for enhancing the ABC technique by introducing a chaotic bee colony initialization operator. This operator can speed up the optimization process and be used as a hyperparameter selection for LSTM neural networks. The overarching objective is to automatically adjust the LSTM neural network's numerous combinations of hyperparameters when forecasting various futures commodities, allowing the model to withstand market fluctuations better and adjust to changing market trends. The significance of hyperparameters in neural network training is emphasized in much research. For instance, [PERSON] et al. [11] claim that choosing the right hyperparameters is primarily responsible for the LSTM's performance. Similarly, [PERSON] et al. [12] contend that hyperparameters yield substantial influence over machine learning algorithms, and that optimizing them can be computationally costly. They suggest using a Q-learning approach to find optimal neural network hyperparameter setups. Moreover, the hyperparameter exploration LSTM predictor (HELP), a superior stochastic exploration technique introduced by [PERSON] et al. [13], further corroborates the profound impact of LSTM hyperparameters on neural network performance. Furthermore, [PERSON] et al. [14] employ advanced techniques for hyperparameter adjustment to evaluate model efficacy, demonstrating how neural network hyperparameters affect the recognition of handwritten digits. The originality of this study lies in its innovative use of a tent chaotic mapping artificial bee colony (TCM-ABC) technique to optimize the hyperparameters of LSTM neural networks, including critical parameters such as window size and neuron count. By harnessing the capabilities of the ABC algorithm, this study endeavors to find robust LSTM hyperparameters within vast decision spaces, thus enabling the effective training of neural networks for the precise prediction of futures commodity prices.