In this article, we focus on'Time Series Data' which is a part of Sequence models. In essence, this represents a type of data that changes over time such as the weather of a particular place, the trend of behaviour of a group of people, the rate of change of data, the movement of body in a 2D or 3D space or the closing price for a particular stock in the markets. Analysis of time series data can be done for anything that has a'time' factor involved in it. So what can machine learning help us achieve over time series data? It can also be used to predict missing values in the data. There are certain keywords that always come up when dealing with time series data. The time series has a specific direction in which the data is moving in.

See also Werbos, Paul J. (1994). The Roots of Backpropagation. From Ordered Derivatives to Neural Networks and Political Forecasting. New York, NY: John Wiley & Sons, Inc. Rumelhart, David E.; Hinton, Geoffrey E., Williams, Ronald J. (8 October 1986). "Learning representations by back-propagating errors". Nature323 (6088): 533–536. doi:10.1038/323533a0.Ph.D. thesis, Harvard University.