A Smart Technique to Forecast Karachi Stock Market Share-Values using ARIMA Model

The stock market index value is a useful tool for investors, public businesses, and governments to invest money while considering the potential for profit and danger of loss. In financial data analysis, the prediction is applied widely to enhance the accuracy of forecasting individual stock indexes and correlation to other indices of other stock market companies. This paper analyses and forecasts time series over the specific span of days aiming at the Karachi stock market. There are different attributes of datasets like Symbol, Date, Open, High, Low, Close and Volume; each attribute has a significant description that plays an essential role in the machine learning analysis. Dicky-fuller statistics have been applied to convert the data into stationary time series. It is used to analyze the stock statistics behaviour, extract trends and seasonality. Time difference lag is used to smooth the data. The data is decomposed to remove trends and seasonality after the transformation in the data. Model diagnostics are applied to analyze the fitness of the model on the data. Time series data prediction is performed by applying the Seasonal ARIMA predictor in conjunction with Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF).