Abstract
Event Detection from data streams is challenging due to the characteristics of such streams, where data elements arrive in real-time and at high velocity, as well as being streams of unbounded size, even more it is not possible to backtrack over the past arrived data elements or review and keep track of the entire history. Financial time-series streams are a source of financial data (tick data) at fine time scales. In this research, we aim to detect events occurring within time-series streams, and our approach utilises the Directional Change Approach, which summarizes price movements based on a given threshold to detect events. In this paper, we propose a dynamic threshold definition method to be used for detecting the directional change events. The threshold is calculated on a daily basis based on previous day price transitions and the current day opening price. An experiment was run for more than 30 weeks to detect the occurring directional change events on a time-series stream, with one minute data flow levels (one minute frequency) to test our threshold definition method against different fixed threshold values. The detected events were evaluated against news headlines published regarding the studied share on the same day the event was found. The results revealed that a daily dynamic calculated threshold more accurately detected events than different static fixed threshold values.