18882865. METHOD AND SYSTEM FOR TIME SERIES FORECASTING INCORPORATING SEASONAL CORRELATIONS USING SCALABLE ARCHITECTURE (Tata Consultancy Services Limited)

METHOD AND SYSTEM FOR TIME SERIES FORECASTING INCORPORATING SEASONAL CORRELATIONS USING SCALABLE ARCHITECTURE

Organization Name

Tata Consultancy Services Limited

Inventor(s)

Avinash Achar of Chennai IN

Soumen Pachal of Chennai IN

Nancy Bhutani of New Delhi IN

Akash Das of Kolkata IN

Subhadeep Banik of Kolkata IN

METHOD AND SYSTEM FOR TIME SERIES FORECASTING INCORPORATING SEASONAL CORRELATIONS USING SCALABLE ARCHITECTURE

This abstract first appeared for US patent application 18882865 titled 'METHOD AND SYSTEM FOR TIME SERIES FORECASTING INCORPORATING SEASONAL CORRELATIONS USING SCALABLE ARCHITECTURE

Original Abstract Submitted

The disclosure herein relates to a method and system for time series forecasting incorporating seasonal correlations using scalable architecture. The scalable architecture comprises parallel encoders, a neural network layer and a decoder. The neural network layer is either an attention layer or RNN layer. Each of the encoders and the decoder comprises multiple sequential encoder and decoder units, respectively. The parallel encoders encode seasonal correlations in a time series to generate summary vectors which are then processed along with state of a previous decoder unit by the neural network layer to generate a feature vector whose size is independent of order of seasonality of the time series. The feature vector is then processed by a next decoder unit to forecast a seasonal time series data at a subsequent time step. This process is repeated for all the decoder units to train the deep neural network model for time series forecasting.