5 Applications of Time Series Analysis

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5 APPLICATIONS OF TIME SERIES ANALYSIS
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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.
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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.
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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.
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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.
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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.
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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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