Time series is applied in various industries. IoT sensors, industrial machines, mathematical finance, weather forecasting, earthquake, electroencephalography, engineering, astronomy, communications engineering, control devices, statistics, signal processing, pattern recognition, econometrics, and other domains of applied science and engineering involving temporal measurements.

Time series is applied in industries like retail, finance and economics. These fields frequently use time series since currency and sales are constantly changing. The stock market is a solid example of time series in action, especially with automated trading algorithms.

Time series is a data points sequence indexed or listed or graphed in time order. It is a sequence drawn at the successive and equally spaced points in time and thus is a sequence of discrete-time data. The daily closing value of the Dow Jones Industrial Average counts of sunspots, and heights of ocean tides are examples of time series.

There are four main components that drive time series. They include:

• Secular trends: They describe the movement with the term. 
• Seasonal variations: These represent seasonal changes.
• Cyclical fluctuations: These correspond to periodical rather than seasonal variations.
• Irregular variations: These are the nonrandom sources of series variations.

Time series analysis is a statistical strategy to analyze the pattern of data points taken over a period of time to forecast the future trends are the major pattern analyzed using time series; it analyzes the increase and decrease of the data series over a certain period.