The stock market offers traders a place to invest and trade in shares since it is a dynamic and volatile marketplace. There are various factors that affect a stock's price. Features that are static and dynamic. For traders, being able to forecast the future value of a specific company's stock can be quite advantageous. An input sequence can be mapped to an output sequence using seq2seq modelling. In this research, we propose a method that uses Bi-Directional LSTM based on sequential Modelling to forecast the future Open, High, Close, Low, Volume (OHCLV) value of a stock.Each OHCLV pricing is a separate sequence, and multitask learning aids in illustrating their connections. It is also suggested to use shared workloads and subtasks in a multitasking system to model prices. Stock prices of APPLE from the Yahoo Finance is used. To evaluate the efficiency of the proposed systems, they are compared against various machine learning algorithms. The proposed Sequential multitasking systems comfortably outperform the existing algorithms

Stock market,Stock prediction, LSTM, BiLSTM, Sequential modeling, Multitasking.