# Crude Oil Price Forecasting Model Based on Neural Networks and Error Correction
## 3 Experiments
### Supplementary Experiments  
Emergencies, including geopolitical conflicts, extreme weather, and financial crises, trigger sharp fluctuations in crude oil prices. These fluctuations often result in atypical trading behaviors and exert significant impacts on supply and demand. For forecasting models, such extreme conditions challenge their ability to learn from historical patterns and assess their robustness and adaptability to abrupt changes. Therefore, this section specifically employs crude oil price data encompassing periods of geopolitical conflict. By evaluating the proposed model's predictive accuracy under severe price fluctuations, we more effectively gauged its capacity to handle volatile conditions. To further verify the efficiency of the model, we selected advanced forecasting methods as baselines, such as [PERSON]'s model [44], [PERSON]'s model [39], and [PERSON]'s model [45]. Table 12 presents the evaluation indicator of the experimental models. Figure 10 shows the original crude oil price data of supplementary experiments and Figure 11 is the variation trend of forecasting results. From these results, we can obtain the following conclusions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **MAE (USD)** & **MAPE (\%)** & **RMSE (USD)** \\ \hline TCN & 2.3003 & 2.8941 & 3.3531 \\ GRU & 2.9344 & 3.4514 & 5.1989 \\ ESN & 2.4249 & 2.9076 & 4.4565 \\ [PERSON]’s model & 0.4926 & 0.5575 & 1.1428 \\ [PERSON]’s model & 0.5827 & 0.6596 & 1.3803 \\ [PERSON]’s model & 0.4270 & 0.5446 & 0.7404 \\ Model 9 & 0.3510 & 0.4420 & 0.5818 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Evaluation indicators for experimental models.  
Figure 9: Prediction results for experimental models across the WTI crude oil price series #3.  
1. In all cases, the forecasting precision of the hybrid model was better than that of the base predictor. This indicates that because the crude oil price data showed obvious chaos and fluctuation, it was difficult for a single model to accurately capture the changes in crude oil price, especially at mutation points. Hence, it was necessary to adopt an effective hybrid model to achieve precise forecasting for the crude oil price.
2. Compared with other state-of-the-art models, the EWT-SARSA-TGE-ELM captured the changes at the mutation points more accurately. The main reason is that our model fully combines the advantages of single algorithms, which makes the model more robust and generalizable. EWT adaptively decomposed the original data into multiple sub-sequences to decrease the nonlinearity and randomness of raw data. In addition, the RL-based ensemble method made the optimal weight decision and combined different predictors to achieve the complementary advantages of base predictors. In addition, the ECM well extracted the predictable components hidden in residuals. In the context of the geopolitical conflict, the ECM focused on the leftover signals in residuals that reflected abrupt changes in price trends. A more accurate prediction could be obtained by overlaying the correction output from the ECM with the prediction from the ensemble module. Hence, our proposed model has potential application in crude oil price forecasting.  
Figure 11: Prediction results for experimental models.  
Figure 10: Raw crude oil price series during geopolitical conflicts.