Machine Learning : Beginning of a New Era in the Dominance of Statistical Methods of Forecasting

This century is one of artificial intelligence (AI). Machines are consistently and continuously trying to learn and imitate human behaviour and thinking. The difference between machines and human beings is that machines are good followers but bad masters due to lack of intelligence within them. Now, this wall is breaking down, and machines are getting brains in the form of neurological thinking. So, the neural network is one of the topics that come under machine learning (ML), used for the purpose of learning from past data to forecast the future is replacing the statistical models of forecasting. Even ML work on AI has better predictive power than statistical tools of forecasting. The superiority of ML somehow may have found its utilization of non-linear algorithms to do so, though ML is computationally more complicated can be only solved with the help of computer science. So, ML is the starting of a new era, by blending computer science algorithms with statistics. This has brought a new dimension of thinking to the machines to forecast future more accurately. Studies of forecasting by ML have not yet achieved the forecasting goals, leaving a question mark on the subject. Simply being new, or based on AI, is not enough to persuade users of their practical advantages over alternative methods. This topic briefly covers the popular empirical studies covering statistical and ML (AI) methods used for the purpose of forecasting and their outcomes and suggestion for the future scope of study. Therefore, this topic will become a base for future study in this field.