Tata Consultancy Services Limited

Narendhar Gugulothu

Gautam Shroff

Lovekesh Vig

Pankaj Malhotra

Puneet Kumar Agarwal

Easwara Naga Subramanian

Sanjay Purushottam Bhat

Vishnu T V

Vishnu Tv

This disclosure relates generally to system and method for time series prediction using a sparse recurrent mixture density network (RMDN), such as sparse LSTM-MDN and a sparse ED-MDN, for accurate forecasting of a high variability time series. The disclosed sparse RMDN has the ability to handle high-dimensional input features, capture trend shifts and high variability present in the data, and provide a confidence estimate of the forecast. A high-dimensional time series data is passed through a feedforward layer, which performs dimensionality reduction in an unsupervised manner by inducing sparsity on weights of the feedforward layer. The resultant low-dimensional time series is fed through recurrent layers to capture temporal patterns. These recurrent layers also aid in learning latent representation of the input data. Thereafter, a mixture density network (MDN) is used to model the variability and trend shifts present in the input and it also estimates the confidence of the predictions.

Time series prediction with confidence estimates using sparse recurrent mixture density networks

Anomaly detection from time series is one of the key components in automated monitoring of one or more entities. Domain-driven sensor selection for anomaly detection is restricted by knowledge of important sensors to capture only a certain set of anomalies from the entire set of possible anomalies. Hence, existing anomaly detection approaches are not very effective for multi-dimensional time series. Embodiments of the present disclosure depict sparse neural network for anomaly detection in multi-dimensional time series (MDTS) corresponding to a plurality of parameters of entities. A reduced-dimensional time series is obtained from the MDTS via an at least one feedforward layer by using a dimensionality reduction model. The dimensionality reduction model and recurrent neural network (RNN) encoder-decoder model are simultaneously learned to obtain a multi-layered sparse neural network. A plurality of error vectors corresponding to at least one time instance of the MDTS is computed to obtain an anomaly score.

Sparse neural network based anomaly detection in multi-dimensional time series

This disclosure relates generally to health monitoring of systems, and more particularly to monitor health of a system for fault signature identification. The system estimates Health Index (HI) of the system as time series data. By analyzing data corresponding to the estimated HI, the system identifies one or more time windows in which majority of the estimated HI values are low as a low HI window, and one or more time windows in which majority of the estimated HI values are high as a high HI window. Upon identifying a low HI window, which indicates an abnormal behavior of the system being monitored, based on a local Bayesian Network generated for the system being monitored, an Explainability Index (EI) for each sensor is generated, wherein the EI quantifies contribution of the sensor to the low HI. Further, associated component(s) is identified as contributing to abnormal/faulty behavior of the system.

Method and system for health monitoring and fault signature identification

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Noida, India

Narendhar Gugulothu