Importance of Time Series Analysis in Data Science


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Time series analysis is deployed in a huge variety of contexts to understand how specific metric changes over a period of time, and to forecast future values. It is well suitable for non-stationary data – things that are regularly fluctuating over specific periods. Finance, economics, and retail industries regularly use time series analysis because both currency and sales always differ.

Stock market analysis is the best example of time series analysis with automated trading algorithms. Similarly, it is useful for predicting weather changes, giving clear information starting from tomorrow’s weather insights to upcoming years of climate change. This report will be used by meteorologists to predict weather forecasting.

How time series data is different from other data? This question strikes your mind when you hear the term time series. The only difference is it displays how a set of variables change over time. Time series analysis needs bulk data points to ensure both reliability and consistency.

How Do You Analyze Time Series?

Time series analysis involves the following steps:

  1. Data collection, and cleaning
  2. Preparing data visualization with reference to time vs major feature
  3. Understanding and stationarity of the series
  4. Generating charts to understand its work
  5. Building AR, MA, ARIMA, and ARMA models
  6. Generating actionable insights from prediction

Time series analysis is useful in:

  • Measuring rainfalls
  • Reading temperatures
  • Monitoring heart rate (EKG), measuring cholesterol, tracking blood pressure
  • Predicting quarterly sales
  • Observing monthly sunspots, and global temperatures
  • Brain monitoring (EEG)

Time series forecasting is used by organizations to predict the probability of upcoming events. It is one of the predictive analytics techniques, which displays possible modifications in the data such as cyclic or seasonality behavior that offers a clear insight related to data variables, and helps in better forecasting.

Different models of time analysis are:

  • Classification: Finds and allocates categories to the data.
  • Curve fitting: Puts the data along a curve to examine the connection of variables between the data.
  • Descriptive analysis: Finds different patterns like trends, seasonal variation, or cycles in time series data.
  • Explanative analysis: Tries to understand the relationships within data, cause, and effect.
  • Exploratory analysis: Showcases the main characteristics of the data in a visual format.
  • Intervention analysis: Examines how data can be affected by events.
  • Segmentation: Divides the data into segments to display the underlying properties of the original information.

Advantages of Time Series Analysis

  • Cleaning Data:

Time series analysis helps in data cleaning by removing outliers and filtering out the noise. With this, it is possible to identify the relevant signal in a data set.

  • Understanding Data:

With the help of time series analysis, analysts can easily understand a data set because the models used in it assist to interpret the real meaning of data.

  • Predict the Future:

Time series forecasting techniques help analysts gain useful information about the future.

Disadvantages of Time Series Analysis

  • The major disadvantage of time series analysis is it could result in wrong predictions because it is used for short-term forecasting.
  • It doesn’t provide the right project value.
Udayan Kelkar
Vice-president of global growth and sales at Express Analytics. Has over 30 years of experience in products/service/sales management and business development. Have a track record in developing fresh business and growing existing client relationships, across multiple geographies.


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