Abstract: A method and product for identifying and categorizing pivot points in a time series, such as a time series of financial data (e.g. stock prices) or medical data (e.g. ECG, EEG). The method permits categorization of pivot points in a time series according to their spatial importance and temporal occurrence. The time series has a predetermined end point, predetermined maximum and minimum box sizes, and a predetermined box size increment. The method consists of determining a breakout direction for the time series. Then, for each incremental box size from the maximum to the minimum, pivot points are identified. Commencing at the end point and working backwards through the time series, any point where a reversal of more than the current box size occurs is identified. For each such identified point, the previous extreme, its associated lag from the end point, and the box size at which it is first identified is then recorded.

Abstract: A method of developing a rule-constrained statistical pattern recognizer applicable to price formation recognition includes assembling input data containing examples of patterns to be recognized and establishing mandatory recognition rules. The recognition rules are programmed to construct an underspecified or underconstrained recognition model which is applied to the assembled data to produce candidate patterns. The candidate patterns are reviewed and identified as valid or invalid and for each pattern type a residual statistical model is produced based on the candidate patterns identified as valid. A filter is used to ensure that custom conditions such as duration relationships, height relationships and volume requirements are met.

Abstract: A method of providing a financial event identification service using a database of fundamental event data or technical event data comprises: receiving a request for fundamental event data or technical event data from a from a client application; querying the database based on the request and client application specific selection criteria to obtain suitable fundamental event data or technical event data; and transmitting the fundamental event data or technical event data to the client application.

Abstract: A method for generating markup information and annotating a time series chart to display recognized pattern formations. Pivot points in the time series are identified and categorized. The pivot points are then analyzed to recognize desired pattern formations. The time series is then graphically displayed with the pivot points marked and labeled. Lines drawn between the pivot points display the recognized pattern to a user. Breakout (trend) lines can also be included. The time series can include time series of financial data, such as stock prices, medical data, such as electrocardiogram results, or any other data that can be presented as a time series, and in which it is desirable to identify turning points, trends, formations or other information.

Abstract: A method of developing a rule-constrained statistical pattern recognizer applicable to price formation recognition includes assembling input data containing examples of patterns to be recognized and establishing mandatory recognition rules. The recognition rules are programmed to construct an underspecified or underconstrained recognition model which is applied to the assembled data to produce candidate patterns. The candidate patterns are reviewed and identified as valid or invalid and for each pattern type a residual statistical model is produced based on the candidate patterns identified as valid. A filter is used to ensure that custom conditions such as duration relationships, height relationships and volume requirements are met.