Time Warping Algorithms and its Applications on Financial Time Series

Abstract

We introduce some of the methods for time warping, which is a technique normally used in speech recognition. Discrete time warping genetic algorithm (dTWGA) is a method based on genetic algorithm, which has been commonly used in solving optimization problems when the solution space is large and when there are no analytic for such solutions. Another method, known as dynamic time warping (DTW), makes use of dynamic programming and involves additional constraints compared to dTWGA. We illustrates the use of dTWGA on construction of _nancial network. We then apply DTW on financial time series for the purpose of portfolio management. In addition to time warping techniques, we also make use of signal detection theory and concepts borrowed from fuzzy set theory in incorporating technical patterns or chart patterns used by traders and technical analysts into some objective trading strategies in a quantitative approach as contrasted to the usual practice by traders which can be seen as an objective and qualitative approach in predicting the trend of price.