Encoder decoder based rnn learning for multi-step time series forecasting in presence of missing data

Abstract

This disclosure relates generally to a method and system for encoder decoder based RNN learning for time series forecasting in presence of missing data. The present disclosure employs atleast one multi-layer RNN encoder and a decoder for time series forecasting. The method receives a plurality of input data comprising a sequential data transformed into a plurality of windows to obtain a plurality of features. Further, the plurality of features is segregated into an available data blocks and a missing data blocks. The first multi-layer RNN encoder fetches the available data blocks and the second multi-layer RNN encoder fetches the missing data blocks to forecast the target variable from the multi-step time series data. The decoder input is generated by appending the combined context vector with an exogeneous variable at each time step.