Grid search with a weighted error function: Hyper-parameter optimization for financial time series forecasting

Financial time series forecasting is a difficult task due to the complexity and volatility of financial markets. Machine learning models have been applied to tackle this task, but finding their optimal hyper-parameters with less time and ensuring the prediction accuracy of models are still significant challenges. Existing methods such as GridSearch with cross-validation (GridsearchCV) for optimizing the hyper-parameters are time-consuming for complex models or large search spaces, and they do not ensure that the model has excellent predictive accuracy. To address these challenges, we propose a novel method called GridsearchWEF that uses grid search with a weighted error function. This method aims to reduce the time cost of hyper-parameter optimization for machine learning models and guarantee their prediction performance. We conduct an empirical analysis of crude oil return forecasting using four machine learning models: RF, GBDT, SVR, and LASSO. We compare the performance of these models using GridsearchCV, random search with cross-validation (RandomizedSearchCV), Bayes optimization with cross-validation (BayesSearchCV), and GridsearchWEF. The results show that GridsearchWEF outperforms the other methods in terms of hyper-parameter optimization, modeling efficiency, prediction accuracy, and economic values. In particular, the time of all models using GridSearchWEF is less than 30s, which is much less than other algorithms. GridsearchWEF is a more efficient and superior method for hyper-parameter optimization in financial time series forecasting.