Stock Price Prediction Using Time Series

The stock price of a commodity is an essential factor for determining market volatility. Exact prediction of stock price and forecasting the market variation are crucial parameters of a stock analyst. The existing conventional approaches are incompetent to predict the stock market variations since they don’t take a comprehensive view but rather look at time-series data for every single stock. In this article, a time series relational model (TSRM) is proposed to predict the stock price. The proposed work combines the relationship between market conditions and price variation of a commodity with time. To anticipate stock prices, relationship information is collected using a graph convolutional network (GCN) and long short-term memory (LSTM) is used to extract time series information. This study attempts to forecast stock prices using the Time series technique, which is appropriate for the financial sector since stock prices fluctuate over time and involve the observation of varied changes regarding any given variable in regard to the respective time.