time series analysis in data science

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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