Principal Component Analysis (PCA) is the focusing on only the ‘principal components’ among the many dimensions of a data set, such that one is reducing the dimensions of that data set by ignoring the ‘non-principal’ parts. PCA though, with eigen values & vectors, take on a slightly deeper approach. Typically, data sets that are handled under PCA are in a matrix format and the principal components, that are sought from a matrix would be a single vector column (or row) that ...
Forecasting the trajectory of the upcoming price movement probably plays one of the key roles in the process of constructing trading plans for the desired planning horizon. The accuracy of such forecasts is critical. In an attempt to improve the quality of trajectory forecasting, we complicate our trajectory forecasting models. more...
Gradient Boosted Decision Trees (GBDT) are a powerful machine learning technique used primarily for regression and classification tasks. They combine the predictions of multiple weak learners, usually decision trees, to create a strong predictive model. The core idea is to build models sequentially, each new model attempting to correct the errors made by the previous ones. Have gained much popularity in the machine learning community as the algorithms of choice for ...
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