# A Survey of Machine Learning Methods for Time Series Prediction
## 7 Conclusions  
This survey of machine learning methods for time series prediction illuminates several key strategies for approaching time series forecasting tasks. Table 3 provides a visual overview of the best-performing models from the research in this study based on the data characteristics chosen. Based on these findings, the following approach is recommended for researchers and practitioners tackling time series prediction problems.  
First and foremost, the domain and data play a pivotal role in determining the success of a forecasting model. Careful domain analysis to identify the most impactful data sources, coupled with meticulous feature engineering to extract and construct relevant features, should be the initial focus. The importance of high-quality data and strong exogenous features cannot be overstated, as evidenced by both the findings of this study and the results of recent forecasting competitions. Investing significant effort in this phase is likely to yield the greatest dividends in the predictive performance. Once the data are prepared, starting with a Tree-Based Machine Learning (TBML) model like LightGBM--or CatBoost for datasets with a high proportion of categorical features--is a logical choice. These gradient-boosting methods offer several advantages: they are computationally efficient, require less feature selection, and have demonstrated a competitive or superior performance compared to deep learning approaches in various settings. Such models serve as a low-cost baseline for experimentation and iterative improvement.  
The model evaluation should be guided by task-specific metrics. For regression problems, metrics such as the Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE) are appropriate, while classification tasks should use Recall, Precision, the F1 Score, and Accuracy. Hyperparameter tuning is another critical component of the modeling process. If prior knowledge exists about potential hyperparameter values, a grid search can be employed for systematic exploration. Otherwise, techniques like Bayesian Optimization or libraries such as OPTUNA are recommended for their ability to efficiently explore large parameter spaces and identify optimal configurations. If the initial model fails to meet the desired performance standards or further enhancements are required, Recurrent Neural Networks (RNNs), such as LSTMs or GRUs, are a logical next step. When building a deep learning model, it is crucial to carefully select and preprocess features to optimize the model's performance and ensure it delivers the best results. As with earlier stages, hyperparameter tuning should be employed to maximize the performance. Finally, combining models--such as the initial TBML model with a deep learning model--can often yield superior results. Ensemble methods leverage the strengths of multiple methodologies,capturing diverse patterns and mitigating weaknesses inherent to any single approach, as demonstrated in this study.  
In conclusion, effective time series prediction is a combination of domain knowledge, data quality, rigorous preprocessing, and the strategic application of the best machine learning methodologies. Starting with scalable and interpretable TBML models, fine-tuning their parameters, and iteratively incorporating advanced techniques like DL provides an efficient framework for tackling time series forecasting challenges. By following this approach, researchers and practitioners can maximize the predictive accuracy while optimizing computational costs in a variety of domains.