Dynamic Time Interval Data Representation in Scalable Financial Time Series Pattern Recognition

Abstract

Mining of scalable patterns hidden in financial time series is a challenging pattern recognition task in nature. Patterns of price movements in financial time series are highly scalable and highly dimensional, making pattern recognition in time series a computational complex data mining effort. Thus, data representation plays a very crucial role in ensuring the effectiveness of time series pattern recognition algorithm. A robust data representation technique not only reduces the dimensionality of the original time series data, but also retains the critical features of the original time series. This study proposes a dynamic time interval data representation approach that adapts to the nature of price movement in financial time series. Since the interval is determined by the price movement magnitude, the proposed data representation approach is more responsive in capturing the major price movements, yet maintaining its agenda in reducing the dimensionality of the raw financial time series data.