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1. 5 APPLICATIONS OF TIME SERIES ANALYSIS

2. Introduction A time series analysis is, at its most basic, the process of analysing an observation of data points collected over a period of time, i.e. time series data. In time series analysis, data analysts record data observations at regular intervals over a set of time periods rather than at random. The observation rate (time interval) can range from milliseconds to several years.

3. Time series in Financial and Business Domain The majority of financial, investment, and business decisions are based on future changes and demand forecasts in the financial domain. Time series analysis and forecasting are critical processes for explaining financial markets' dynamic and influential behaviour. An expert can forecast required forecasts for important financial applications such as risk evolution, option pricing and trading, portfolio construction, and so on by analysing financial data.

4. Time series in Medical Domain In the medical domain, it is more important to examine the evolution of behaviour over time than to draw conclusions based on absolute values in time series. For example, using sensor readings to diagnose heart rate variability in conjunction with respiration is a typical example of connecting time series with case-based monitoring.

5. Time Series in Astronomy Different areas of astronomy and astrophysics are among the contemporary and modern applications where time series play a significant role. Being specific in its domain, astronomy hugely relies on plotting objects, trajectories and accurate measurements, and due to the same, astronomical experts are proficient in time series in calibrating instruments and studying objects of their interest.

6. Time series in Forecasting Weather Many governments have established thousands of weather forecasting stations around the world in order to conduct atmospheric measurements using computational methods for fast compilations. These stations are outfitted with highly functional devices and are linked together to collect weather data at various geographical locations and forecast weather conditions at any given time.

7. Conclusion Time series are characteristics of complex datasets that are recorded over a fixed period of time. Time series analysis, modelling, and forecasting are important in real-world applications and a wide range of research fields such as business, economics, medicine, astronomy, engineering, politics, and many others.

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