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 formation recognition in technical analysis relies on the use of pivot points. A formation of interest is defined in terms of extreme points. The extreme points can be characterized in relation to one another as a series of local or global extrema in corresponding intervals. The method numbers the pivot points in reverse chronological order and attempts to match pivot points with the extreme points of the formation of interest. The first pivot point is assigned to the first extreme point of the formation. A second pivot point is selected from the interval defined by the first pivot point and the highest numbered pivot point that is a high or a low, as required by the formation. Subsequent pivot points are selected from intervals determined based on the formation and previously determined intervals. A formation is recognized if corresponding pivot points are identified for all extreme points in the formation.

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 formation recognition in technical analysis relies on the use of pivot points. A formation of interest is defined in terms of extreme points. The extreme points can be characterized in relation to one another as a series of local or global extrema in corresponding intervals. The method numbers the pivot points in reverse chronological order and attempts to match pivot points with the extreme points of the formation of interest. The first pivot point is assigned to the first extreme point of the formation. A second pivot point is selected from the interval defined by the first pivot point and the highest numbered pivot point that is a high or a low, as required by the formation. Subsequent pivot points are selected from intervals determined based on the formation and previously determined intervals. A formation is recognized if corresponding pivot points are identified for all extreme points in the formation.

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.