outputs/Why is feature engineering important in financial forecasting/research--8497491436117821687.txt

[{"title": "Feature Engineering for Predictive Modeling: A Guide - LinkedIn", "href": "https://www.linkedin.com/advice/3/how-do-you-use-feature-engineering-improve", "body": "Learn more \u2014 The LinkedIn Team Last updated on Apr 27, 2023 Feature engineering is a crucial step in predictive modeling, where you transform raw data into meaningful and useful features for..."}, {"title": "Survey of feature selection and extraction ... - Financial Innovation", "href": "https://jfin-swufe.springeropen.com/articles/10.1186/s40854-022-00441-7", "body": "Different types of technical in- dicators, such as stochastic oscillator, moving averages, and relative strength index (RSI), are used in prediction models, and the effectiveness of these input features for future stock market forecasting is studied."}, {"title": "Discrete Wavelet Transform-based feature engineering for ... - Springer", "href": "https://link.springer.com/article/10.1007/s41870-023-01157-2", "body": "CSO is used for the feature selection and to get the optimal feature set. As per the authors' best knowledge none of the existing studies had used CSO for feature selection in financial TS analysis. The proposed feature engineering component is named as DWT-CSO. For stock market trend prediction, ML and DL models are used."}]