Narendhar Gugulothu

Title: Innovations by Narendhar Gugulothu

Introduction

Narendhar Gugulothu is a prominent inventor based in Noida, India. He has made significant contributions to the field of time series prediction and anomaly detection through his innovative patents. With a total of three patents to his name, Gugulothu is recognized for his advanced methodologies that enhance forecasting accuracy and anomaly detection in multi-dimensional time series data.

Latest Patents

One of his latest patents is titled "Time series prediction with confidence estimates using sparse recurrent mixture density networks." This invention relates to a system and method for time series prediction utilizing a sparse recurrent mixture density network (RMDN). The RMDN, including sparse LSTM-MDN and sparse ED-MDN, is designed for accurate forecasting of high variability time series. It effectively handles high-dimensional input features, captures trend shifts, and provides confidence estimates for forecasts. The process involves passing high-dimensional time series data through a feedforward layer for dimensionality reduction, followed by recurrent layers that capture temporal patterns and learn latent representations of the input data.

Another notable patent is "Sparse neural network based anomaly detection in multi-dimensional time series." This invention addresses the challenges of anomaly detection in automated monitoring systems. It introduces a sparse neural network for detecting anomalies in multi-dimensional time series data corresponding to various parameters of entities. The approach utilizes a dimensionality reduction model to obtain a reduced-dimensional time series, which is then processed through a recurrent neural network encoder-decoder model to create a multi-layered sparse neural network. This innovation computes error vectors to derive an anomaly score, enhancing the effectiveness of anomaly detection.

Career Highlights

Narendhar Gugulothu is currently employed at Tata Consultancy Services Limited, where he applies his expertise in developing advanced technological solutions. His work focuses on leveraging machine learning and neural networks to solve complex problems in data analysis and prediction.

Collaborations

Some of his notable coworkers include Pankaj Malhotra and Lovekesh Vig, who collaborate with him on various projects within the company.

Conclusion

Narendhar Gugulothu's innovative contributions to time series prediction and anomaly detection demonstrate his commitment to advancing technology in these fields. His patents reflect a deep understanding of complex data systems and a drive to improve forecasting accuracy and anomaly detection methodologies.