Analysis and prediction of closing price of commodity index using auto regressive integrated moving averages (ARIMA) model

In most of the developed nations, there are markets where shares, securities or commodities are traded on daily basis, which generally reflects the growth of any country and the health of the company stocks which are traded.Stock market investment is considered to be one of the riskiest investments by investors all around the world, but if historical data is studied carefully then the gap between how the market behaves and what investors know, can be minimized.The data of opening and closing price, which is generated, is a times-series in nature. This time-series data of any index or stock attracts researchers to predict the next move or price of the commodity or index.There are lots of methods available to analyze the data like auto-regressive integrated moving average (ARIMA), regression based, neural network or moving averages methods.However, ARIMA is a type of model which is generally applied to the time-series data to gain insights into data,to understand what happened in the past and what is the next move to expect. In this chapter, an attempt has been made to use the time-series data of the past 5 years and based on that we can forecast the future direction of the indices.