A time series is a sequence of data points measured at successive points in time spaced at uniform time intervals. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance and many more. Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistic and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. Regression analysis is often employed in a way to test theories that the current values of one or more independent time series affect the current value of another time series, this analysis of time series is not called “time series analysis”. It focuses on comparing values of time series at different points in time.

Time series data have a natural temporal ordering. It makes time series analysis distinct from other data analysis problems in which there is no natural ordering of the observations. Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations. Time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values. Time series analysis can be applied to real-valued, continuous data, discrete numeric data or discrete symbolic data.

Models for time series data can have many forms and represent different processes. When modeling variations in the level of a process, the three broad classes of practical importance are the autoregressive models, the integrated models and the moving average models.

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