Machine learning and time series: Real world applications

Abstract

There are in-numerous applications that deal with real scenarios where data are captured over time making them potential candidates for time series analysis. Time series contain temporal dependencies that divide different points in time into different classes. This paper aims at reviewing marriage of a concept i.e. time series modeling with an approach i.e. Machine learning in tackling real life problems. Like time series is ubiquitous and has found extensive usage in our daily life, machine learning approaches have found its applicability in dealing with complex real world scenarios where approximation, uncertainty, chaotic data are prime characteristics.