Financial time series prediction often utilizes dimensionality reduction techniques due to high feature correlations. While PCA is commonly employed, it can dilute individual variable impact, complicating tasks like feature selection. To tackle this, Forward Selection Component Analysis (FSCA) offers a more refined method by selecting variables based on their unique variance contribution. FSCA's backward refinement step further optimizes variable choice by replacing less important ones, enhancing model accuracy without sacrificing step order. These techniques empower traders and developers to build more transparent and effective algorithmic trading models, highlighting how FSCA addresses PCA's limitations in highly correlated datasets.
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Mutual information is a potent tool for feature selection, offering unique insights into dependencies in complex, nonlinear datasets. The technique excels by identifying predictors that traditional methods might overlook. This article introduces the application of mutual information in identifying effective predictors, emphasizing the algorithm proposed by Peng, Long, and Ding. The focus is on the accurate estimation of mutual information for continuous variables through adaptive partitioning and the Parzen window method, demonstrating how these techniques overcome the pitfalls of fixed binning. With practical code examples in MQL5, traders and developers can implement these methods for enhanced model performance, balancing relevance with redundancy in predictor selection.
#MQL5 #MT5 #Algorithm #FeatureSelection

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