Cognizant Technology Solutions India Pvt. Ltd.

Mrityunjoy Panday

Nagarajan Kumar

The present invention provides for a system and a method for optimised time series forecasting. A time-series dataset is converted corresponding to a system, for which forecast data is to be determined, into data embeddings in the form of a distance vector. A hierarchical clustering of values of the distance vector is performed, wherein the hierarchical clustering comprises creating a high-level cluster by combining two or more local clusters. A hierarchical tree is created based on the hierarchical clustering, wherein the hierarchical tree represents a first level cluster and a second level cluster. A plurality of factors is extracted from each node of the tree and a gaussian process decomposition is applied on the extracted factors from each node of the tree to determine decomposed factors. The decomposed factors represent interpretable components of the extracted factors and a forecast data is determined for system based on decomposed factors.

System and method for hierarchical factor-based forecasting

A System and a Method for Assessing Data for Analytics A system and a method for assessing readiness of data for data science and analytics is disclosed. The present invention quantifies readiness of data by providing a data insight quotient (DIQ). In particular, a nucleus of a dataset retrieved from an enterprise database is derived. The dataset is representative of data collected for a predefined objective over a period of time and includes a plurality of features and records. Further, a plurality of time-ordered datasets are generated by dividing the retrieved dataset based on a selected time series. A nucleus is derived for each of the time-ordered dataset. Furthermore, relevancy and redundancy of each of the plurality of time-ordered datasets is evaluated. The present invention, further computes the complexity and noise associated with each of the time-ordered datasets. Finally, a DIQ value for each time-ordered dataset is evaluated as a function of relevancy, complexity, noise and redundancy.

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Kolkata, India

Mrityunjoy Panday