Tata Consultancy Services Limited

Suvra Dutta

Tanushyam Chattopadhyay

Prateep Misra

Shubhrangshu Ghosh

Abhisek Das

Pranav Champaklal Shah

Suman Datta

Sangram Dasharath Gaikwad

Rekha Vaidyanathan

Tania Ghosh

Arindam Halder

State-of-the-art approaches have concentrated on building solution(s) to match the amplitude of a time series with a user given one. However, these have failed to implement solution(s) which enables searching for pattern(s) that can depict human vision psychology. Embodiments of the present disclosure determine occurrence of pattern of interest in time series data for anomaly detection, wherein time series data is obtained, and first order derivative is computed. Further an angle of change in direction is derived based on a gradient of change in value of the time series data. This angle is further converted to a measurement unit. The time series data is quantized into bins and a weighted finite state transducers diagram (WFSTD) is obtained based on domain knowledge which is then converted to specific pattern. The specific pattern is searched in the bins to determine occurrence/count of the specific pattern for anomaly detection.

Systems and methods for determining occurrence of pattern of interest in time series data

Sensor data (or IoT) analytics plays a critical role in taking business decisions for various entities (e.g., organizations, project owners, and the like). However, scaling of such analytical solutions beyond certain point requires adopting to various computing environments which seems to be challenging with the constrained resources available. Embodiments of the present disclosure provide system and method for analysing and executing sensor observational data in computing environments, wherein extract, transform, load (ETL) workflow pipeline created by users in the cloud, can be seamlessly deployed to job execution service available in cloud/edge without any changes in the code/config by end user. The configuration changes are internally handled by the system based on the selected computing environment and queries are executed either in distributed or non-distributed environments to output data frames. The data frames are further pre-processed in a desired computing environment and thereafter visualized accordingly.

Systems and methods for recommending execution environment for analysing sensor observational data

The present application provides a method and system for outlier detection, anomalous behavior detection, missing data imputation and prediction of consumption in energy data for one or more energy sensors by using a unified model. The application discloses a data collection module for collect a time series data to be used as training data, a model training module for training the unified model using the collected time series data to enable computation of a plurality of parameters, and a model implementation module for implementing, by the trained unified model, the plurality of parameters on a new data of energy consumption wherein the plurality of parameters are used perform at least one from a group of outlier detection, anomaly detection, missing data imputation and prediction of consumption in energy data.

Method and system for anomaly detection, missing data imputation and consumption prediction in energy data

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Suvra Dutta