Abstract: A method and system for converting non-ordered categorical data stored within a column in a data set into an ordered or continuous data stored in a new column within the data set. Each distinct categorical value in the nominal data column is represented by a corresponding distinct numerical value in the new column. The new representative numerical values are derived by constructing separate time series for each distinct value in the nominal data column and by calculating the similarities between the shapes of the time series. The proximity of the time series is captured in a numeric distance score. Each distinct distance score corresponds to a distinct value in the nominal data column and is a valid representation of that value in machine learning, deep learning, and statistical analysis.

Abstract: A method and system for discovering motifs in time series data from trading activities and using them to predict future trading trends. Each motif contains a set of sequential data points and its shape uniquely describes the trading events for a specified time period. Selected motifs are used as search references to find similar or dissimilar motifs within all or any sub-segment of the time series data and a similarity score is calculated for all matches. An artificial intelligence network learns the relationship between the similarity scores of the motifs and the subsequent trading events. The artificial intelligence network evaluates the shape of any trading motif, compares it with the learned motifs, and generates a prediction for the most likely motif to occur in the next trading period.

Abstract: A method and system for converting non-ordered categorical data stored within a column in a data set into an ordered or continuous data stored in a new column within the data set. Each distinct categorical value in the nominal data column is represented by a corresponding distinct numerical value in the new column. The new representative numerical values are derived by constructing separate time series for each distinct value in the nominal data column and by calculating the similarities between the shapes of the time series. The proximity of the time series is captured in a numeric distance score. Each distinct distance score corresponds to a distinct value in the nominal data column and is a valid representation of that value in machine learning, deep learning, and statistical analysis.

Abstract: A method and system providing the ability to visually explore temporal data, to identify and tag patterns, and to conduct proximity searches for similar patterns across different data sets, dimensions and/or hierarchical levels. Time series patterns manifest themselves at different levels of aggregation. In one embodiment of the invention, the proximity search parameters are derived from an aggregate level trend and are algorithmically adjusted to find matching patterns within detailed records. Further, the method includes means to traverse time series data, to select subsets of time sequences, and to evaluate statistically the said subsets for proximity to the original time sequence. The method and system provide means to connect and browse temporal data in databases, applications, files, and real-time data streams, to monitor for time-series patterns, and to generate event based alerts.