time series analysis in data science

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Time Series Analysis In Data Science
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Table Of Content

1. What is Time Series Analysis ?
2. Why Is Time Series Analysis Important?
3. How Does Time Series Analysis Work?
4. Applications Of Time Series Analysis

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What is Time Series Analysis ?
Time-series analysis is a technique for
analyzing a group of data points across time.
Time series analysts collect data points over a
predetermined time at regular intervals rather
than irregularly or arbitrarily.
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Why Is Time Series Analysis Important?
Organizations can understand systemic patterns
across time by using time series analysis. Data
visualizations allow Customers to examine
seasonal trends and learn more about their
causes. These visualizations can do much more
than display graphs of lines with today's
analytics solutions.
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How Does Time Series Analysis Work?
A particular method of analyzing data points
gathered over time is called a "time series
analysis." Instead of just capturing the data
points intermittently or randomly, time series
analyzers record the data points at regular
intervals over a predetermined time.
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Applications Of Time Series Analysis
Weather forecasting, climate forecasting,
economic forecasting, healthcare forecasting,
engineering forecasting, financial forecasting,
retail forecasting, business forecasting,
environmental studies forecasting, social
studies forecasting, and many more practical
applications are among them.
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