# Nickel and Cobalt Price Volatility Forecasting Using a Self-Attention-Based Transformer Model
## 2 Literature Review  
In their 1995 study on the volatility of metal prices, ref. [7] created a model that connects metal volatility to metal balance. [PERSON] and [PERSON] [8] were the first to examine the fluctuation in prices of crude oil and a variety of industrial metals, including Nickel. They found that the volatility of crude oil for the period of 1985-1994 was comparable to that of Nickel. [PERSON] and [PERSON] [9] used the generalized autoregressive conditional heteroskedastic and Glosten-Jagannathan-Runkle models to investigate the effect of outliers and oil price shocks on the volatility of metal prices. It was discovered that outliers skewed estimate models, and that the price volatility of metals responds asymmetrically and differentially to changes in the price of oil. [PERSON] et al. [10] used monthly Nickel and Zinc prices from the year 2015 to assess the effectiveness of the modified grey wave forecast model. The findings of these two tests showed that, in terms of prediction accuracy and computing efficiency, the modified grey wave approach outperformed the original grey wave method. [PERSON] et al. [11] recommended an approach for forecasting Nickel prices that employed an improved particle swarm optimization (PSO) technique combined with LSTM networks. They compared the performance of classic LSTM networks and ARIMA with the upgraded PSO-LSTM model. The results revealed that the enhanced PSO method could notably enhance the predictive accuracy of the LSTM networks and had a quicker convergence rate. For anticipating changes in the price of gold, [PERSON] et al. [12] suggested a hybrid model combining the whale optimization algorithm (WOA) with multilayer perceptron neural networks (NN). Numerous methods have been applied previously for understanding the forecasting accuracy of metallic commodity prices. For example (support vector machine) with the genetic algorithm to predict gold future prices. Likewise, ref. [13] used SVR for forettelling the prices of copper futures. [PERSON] et al. [14] used a bat algorithm for predicting copper prices. [PERSON] et al. [15], on the other hand, used wavelet-based ARIMA (auto regressive integrated moving average) to forecast metallic prices. Similarly, experts such as [12; 16; 17] used hybrid models including neuro-fuzzy inference systems and genetic algorithms for predicting the forecasting accuracy of various metallic prices. Similarly, ref. [18] advanced the Heterogeneous AR (HAR) model by [19] for increasing the accuracy of price volatilities of commodities. [PERSON] et al. [20] utilized a hybrid neural network that incorporated Bayesian optimization and wavelet transform techniques to predict copper prices over the short- and long-term. In addition, they employed LSTM and GRU networks to analyze the data and predict future copper prices. Based on their research, both LSTM and GRU networks demonstrated accuracy in predicting copper prices over various timeframes. There have been other relevant studies conducted using various deep-learning-based neural networks models. For example, ref. [21] forecasted Nickel prices based on LSTM and GRU and found that both the methods provided significant improvements over previous ones in terms of their accuracy as well as the computation rate. This paper primarily deals with the impact of an energy commodity, namely oil prices on precious metals which are used mainly for clean energy purposes. The reason for the interest is primarily that the oil market is particularly susceptible to fluctuations in market volatility and economic growth, as stated by [22]. This study takes a step further and utilizes more advanced Recurrent Neural Networks (RNNs) that employ self-attention mechanisms such as TFTs (Temporal Fusion Transformers) to analyze their performance in terms of both speed and accuracy. Temporal Fusion Transformers (TFTs) are a variant of the transformer design of [23], a class of neural networks initially developed for natural language processing applications.