# A Survey of Machine Learning Methods for Time Series Prediction
## 5 Experimental Results and Discussion
### Analysis
This suggests that researchers may devote disproportionate attention to optimizing models aligned with their research focus while investing less effort in models they are using for comparative efforts. Readers should exercise caution when interpreting comparative results in such studies. In studies with no specific focus, TBML models slightly edge out DL models, providing an interesting case for their superior performance in \"bias-free\" papers. A potential explanation for this disparity is that the accuracy of DL models is more dependent on the regularization techniques used and hyperparameter values chosen for testing compared to TBML models. When researchers are biased towards DL methods, they may spend more time on this aspect of model development compared to TBML model development, causing DL methods to perform better. If researchers do not invest enough time or effort in this area of model developemnt, then TBML models are more likely to outperform DL methods. Computational Efficiency: One of the most impactful findings is the disparity in training times between TBML and DL models. TBML models were found to be, on average, two-to-four orders of magnitude faster than DL models. This makes TBML models particularly appealing for real-world applications where computational cost is a critical consideration. Error Metrics: Popular error metrics for evaluating classification and regression tasks may provide valuable benchmarks for future research. These include classification metrics such as Recall, Precision, the F1 Score, and Accuracy, as well as Regression Metrics such as RMSE, MAE, MAPE, and MSE. Future research may benefit from aligning evaluation methods with these metrics to ensure comparability across studies. Additionally, researchers may want to consider using a novel weighted quality evaluation index (WQE) proposed by [89], which combines the four most popular regression metrics (RMSE, MAE, MAPE, and R2) into a single unified evaluation standard that more holistically captures the model performance. Hyperparameter Optimization: A grid search is the most frequently used hyperparameter optimization method, but it is also the most computationally intensive. Bayesian Optimization offers a promising alternative, providing a similar performance with much lower computational demands. Methods such as OPTUNA's automatic hyperparameter tuning software are also emerging as viable options for hyperparameter tuning. Hybrid Models: This study finds strong evidence that combining models generally enhances their performance, as evidenced by numerous surveyed papers that implement hybrid approaches. These hybrid models consistently outperform individual models, with their performance often improving as more models are integrated. There are notable exceptions to this trend, discussed in Section 5.3.9, where individual models show a superior performance compared to hybrids. These individual model examples exclusively involve SPTB models, further underscoring the robust performance of these algorithms. When comparing hybrid configurations, the results reveal that combinations of SPTB models with RNN models yield particularly strong outcomes. Additionally, hybrid models incorporating either SPTB or RNN architectures with attention-based or CNN models also demonstrate notable performance improvements. Anecdotal Findings: Beyond the quantitative analysis across all surveyed papers, several anecdotal observations offer additional insights:  
1. Feature Sensitivity: GBDT models are less affected by redundant or removed features, whereas the ANN performance drops significantly when redundant features are added [8];
2. Feature Selection: When all features are provided, XGBoost consistently delivers the best performance. However, when variables are selected using forward selection, other DL models begin to outperform it. Interestingly, the XGBoost model utilizing all features outperforms the XGBoost model that uses only the forward-selected features [15];
3.