A generalized financial time series forecasting model based on automatic
  feature engineering using genetic algorithms and support vector machine
  We propose the genetic algorithm for time window optimization, which is an
embedded genetic algorithm (GA), to optimize the time window (TW) of the
attributes using feature selection and support vector machine. This GA is
evolved using the results of a trading simulation, and it determines the best
TW for each technical indicator. An appropriate evaluation was conducted using
a walk-forward trading simulation, and the trained model was verified to be
generalizable for forecasting other stock data. The results show that using the
GA to determine the TW can improve the rate of return, leading to better
prediction models than those resulting from using the default TW.
