Multivariate Time Series Sensor Feature Forecasting Using Deep Bidirectional LSTM
B. Sirisha, Surakanti Naveena, Greeshma Palanki, Pottipally Snehaa · Procedia Computer Science · 2023
Multivariate Time series data forecasting (MTSF) is the assignment of forecasting future estimates of a particular series employing historic data. Lately, this work has enticed the focus of machine and deep learning researchers to tackle the complex and time consuming aspects of conventional forecasting techniques. Along with increasing access to historic documented data and the dire need of carrying out precise time-series feature forecasting. In this paper, we put forward a deep-learning(DL) technique proficient to tackle the setback of conventional forecasting techniques and display precise forecasting. The proposed technique is a Stacked Bidirectional long-short term memory architecture, which is an improvised version of existing Bidirectional LSTM in which multiple Bidirectional LSTM blocks are stacked, such that each layer contains multiple cells. In the direction of fair assessment, the functioning of the proposed technique is compared with existing LSTMs. Using varied evaluation measures, the obtained results show that the proposed Deep Bidirectional long-short term memory model exceeds standard approaches.