Time Series Data Forecasting System for Stock using TA and ARIMA Model

Time series data forecasting system is a vital research field in today era. It focuses on many real world problems. Among them stock price forecasting and market movement direction is also important. As known, the technical analysis methods, statistical methods and deep learning neural networks models are the most useful model for the prediction problems. Besides they give good results for time series data foresting system. In this paper, combine with simple moving average method, exponential moving average method, Moving Average Convergence Divergence method are used to make decisions for buying or selling the stocks. And then, in this research, Autoregressive Integrated Moving Average model is used to predict stocks prices, stocks trends and to make business strategies. Moreover, the research nearly gives the forecasting time series data and the outcome accuracy is 96.98% accuracy.