A computer-implemented method for aligning intercorrelated asynchronous time series datasets includes the steps of: (a) retrieving a first time series dataset (x) and a second time series dataset (y), the first and second time series dataset being intercorrelated, (b) segmenting each of the first and second time series dataset (x, y) into a plurality of consecutive smaller segments (x.sub.i, y.sub.i), all segments of the first and second time series dataset (x, y) having the same length, (c) determining pairs of corresponding segments by associating successive segments of the first time series dataset (x) with corresponding segments of the second time series dataset (y), (d) optimizing, for each pair of corresponding segments, a correlation function to obtain an approximation of a first times series transformation function (f.sub.1) and of a second time series transformation function (f.sub.2), (e) using the first and second time series transformation functions (f.sub.1,f.sub.2) to determine a vector of segment shifts (s) whose components contain approximations of the shifts between the first and the second segment in a pair of corresponding segments (x.sub.i, y.sub.i), (f) applying a multi-model fitting algorithm to the segment shift vector (s), said multi-model algorithm outputting a shift function (f.sup.opt) for aligning segments of each pair of corresponding segments (x.sub.i, y.sub.i), and (g) aligning the first time series dataset (x) with the second series dataset by applying said shift function (f.sup.opt) to all pairs of corresponding segments (x.sub.i, y.sub.i),
wherein the first time series transformation function (f.sub.1) is parametrized by weights (w.sub.1) of a first neural network (N.sub.1) and outputs a first highly correlated time series dataset, and wherein the second time series transformation function (f.sub.2) is parametrized by weights (w.sub.2) of a second neural network (N.sub.2) and outputs a second highly correlated time series dataset.

Aspects of the invention include determining an event associated with a computing system, the event occurring at a first time, obtaining system data associated with the computing system, determining a system state of the computing system at the first time based on the system data, determining, based on the system state, two or more system data clusters comprising clustered system data associated with the system state of the computing system, determining, via an interpolation algorithm, an interpolated data value for the first time based on the system data, and adjusting the interpolated data value based on a determination that the interpolate data value is outside the two or more system data clusters.

There is provided a method and apparatus for determining a baseline for one or more physiological characteristics of a subject. One or more physiological characteristics of a subject and contextual information associated with the subject is acquired. A time period in which the subject is in a baseline state is detected based on the acquired one or more physiological characteristics of the subject and the acquired contextual information associated with the subject. The baseline for the one or more physiological characteristics of the subject is determined from a value of at least one of the acquired one or more physiological characteristics in the detected time period.

A device that includes a master boss and a slave boss. The slave boss is configured to iteratively send execute and output commands to a first plurality of nodes implemented by a node engine identified in a first boss table in response to receiving an execute command from the master boss. The master boss is configured to iteratively send execute and output commands to the slave boss and a second plurality of nodes implemented by the node engine identified in a second boss table. Each node is configured to receive a first correlithm object, fetch a second correlithm object based on the first correlithm object in response to receiving an execute command, and output the second correlithm object in response to receiving an output command.

Embodiments of the present disclosure are directed to facilitating performing online data forecasting. In operation, data decomposition of an incoming data point is performed to determine a trend component associated with the incoming data point. Such a trend component, and previous trend components, can be used to determine a trend component expected for a data point subsequent to the incoming data point. A seasonality component expected for the data point subsequent to the incoming data point can be identified, for example, based on a seasonality component associated with a previous corresponding data point. Thereafter, the expected trend and seasonality components can be used to predict the data point subsequent to the incoming data point. Such a data prediction can be performed in an online processing manner such that a subsequent data point is not used to decompose the incoming data point or forecast the data point.

An Online Machine Learning System (OMLS) including an Online Explanation System (OES), updated continuously, configured to provide instance level explanations and model level explanations to a user; an Online Human Expert Feedback System (OEFS), updated continuously, configured to obtain expert instance level feedback and expert model level feedback, for optimization of operation of the OMLS; an Online Machine Learning Engine (OMLE) for incorporating and utilizing one or more machine learning algorithms or models utilizing features to generate a result, and capable of incorporating and utilizing multiple different machine learning algorithms or models, wherein the OMLS is configured to perform continuous online machine learning.

An Online Machine Learning System (OMLS) including an Online Explanation System (OES), updated continuously, configured to provide instance level explanations and model level explanations to a user; an Online Human Expert Feedback System (OEFS), updated continuously, configured to obtain expert instance level feedback and expert model level feedback, for optimization of operation of the OMLS; an Online Machine Learning Engine (OMLE) for incorporating and utilizing one or more machine learning algorithms or models utilizing features to generate a result, and capable of incorporating and utilizing multiple different machine learning algorithms or models, wherein the OMLS is configured to perform continuous online machine learning.

A system and a method for entity resolution is disclosed. An entity reference parsing subsystem to parse one or more entity references of a corresponding seed set of entity into corresponding one or more personal data properties and property values. A property value standardization subsystem performs one or more standardization operations for standardization of the corresponding one or more property values. A property value anonymization subsystem secures the one or more property values by performing one or more anonymization procedures. A property strength quantification subsystem identifies at least one additional property suspected to belong to the seed set of the entity, assigns a property strength score to the at least one additional property, adds the at least one additional property to the corresponding seed set of entity. A local entity resolution and a global entity resolution subsystem performs a first and a second entity resolution process respectively.

A system and a method for entity resolution is disclosed. An entity reference parsing subsystem to parse one or more entity references of a corresponding seed set of entity into corresponding one or more personal data properties and property values. A property value standardization subsystem performs one or more standardization operations for standardization of the corresponding one or more property values. A property value anonymization subsystem secures the one or more property values by performing one or more anonymization procedures. A property strength quantification subsystem identifies at least one additional property suspected to belong to the seed set of the entity, assigns a property strength score to the at least one additional property, adds the at least one additional property to the corresponding seed set of entity. A local entity resolution and a global entity resolution subsystem performs a first and a second entity resolution process respectively.

The Marker Fingerprinting system provides a method for identifying and correlating serial or sequential data patterns across diverse domains such as geological, biological, and financial datasets. This innovation transforms single- or multi-attribute data series into feature matrices, generating unique hash tokensor fingerprintsthat encapsulate specific data patterns. Using advanced signal analysis and spectral transformations, it enables efficient processing and pattern recognition within complex datasets. Fingerprints from reference patterns are matched against target datasets, with quantitative confidence metrics derived from weighted algorithms assessing match accuracy. Iterative data conditioning enhances robustness by addressing noise and inconsistencies, ensuring reliability at scale. The invention improves decision-making by delivering rapid and accurate pattern identification with quantified reliability, making it particularly suited for applications like geological top picking, seismic data analysis, and other fields requiring precise data correlation