Time Series is a set of data points, usually collected at uniform or regular intervals of time. Examples of time series data occur generally in a variety of fields such as Economics, Finance, and Medicine. Particularly, the examples of time series data in the field of Economics include Monthly data of unemployment, GDP data, etc. Similarly, time-series data in Finance includes daily exchange rate, stock prices, etc and the data of environmental science includes Daily rainfall data, temperature data, and so on. In order to observe the pattern of any time series data, the most frequently used method is to construct a line chart for the variable against the given unit of time (day, week, month, or year). Time series plots and techniques are used not only in Statistics but also widely used in various fields such as Signal processing which is a branch of System and Electrical Engineering to represent time-varying physical quantities. It is also used in pattern recognition, weather forecasting, prediction of earthquakes, and so on.

Time series can be decomposed into four elements as follows:

  • Trend –> Increase or decreasing pattern in the series over a long period of time.
  • Seasonal effects –> Fluctuations that occur every month, year, etc.
  • Cyclical Fluctuations –> Fluctuations related to economic or business conditions. It is also called as a Business cycle.
  • Residual Effect –> Other random fluctuations excluding Trend, Cyclical and Seasonal components of a time series. It may occur due to various events such as hurricanes, strikes, etc.


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It encompasses many higher-level models to fit time-series data such as Moving Average, Exponential Smoothing, ARIMA models, and so on. Statistical software such as SPSS, Minitab, STATA, SAS, and R includes the tools of all the Time Series models.

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