A Complex Event Processing Toolkit for Detecting Technical Chart Patterns

With the advent of large high volume data, we have seen need for real time analytic techniques like Complex Event Processing. This paper extends a Complex Event Processing Engine to support real time identification of technical chart patterns from streaming data. Technical chart patterns are known interesting recurring patterns on time series data, and they are used by experts in time series data analysis domains such as stock market and currency exchange rates. Yet the automated identification of these patterns is challenging due to the high volatility and noise of data. The paper focuses on identifying suitable technique to filter out volatility and a set of algorithms to query the data streams continuously and to identify patterns. The resulting solution is a toolkit for chart pattern recognition which is a composition of a set of complex CEP queries and a Kernel regression smoother applied on moving windows. Same toolkit can be used to detect chart patterns in other domains like Gold and Oil prices etc as well.