Explain Time Series in Machine Learning?

Time series data is data that is collected at different points in time. This is opposed to cross-sectional data which observes individuals, companies, etc. at a single point in time. Because data points in time series are collected at adjacent time periods there is potential for correlation between observations.

What is meant by Time Series?

Time series is a sequence of data points spread over a specific duration of time, where time is the independent variable and other variables are not constant. The time-series data is analyzed over a regular temporal interval.

Time series data can be helpful in the following cases:

  • Tracking weather data on an hourly, daily, and weekly basis
  • Tracking the performance of websites
  • Predicting earthquakes

The time series has 4 variations like

  • Seasonal variations
  • Trend variations
  • Cyclical variations, and
  • Random variations

What is Time Series Analysis?

Time series analysis is the process of determining the basic patterns disposed by the data over a duration of time. This approach isn’t expensive and is used to forecast business-related metrics including sales, turnover, analysis of the stock market, and budgetary analysis.

Advantages of Time Series Analysis:

  • The time-series data will lead to arduous, and complicated calculations because of its nature which in turn makes forecasting difficult. With the help of Python and R languages, analysts can create and tune perfect time series forecasts with minimal effort.
  • Time series models have fewer assumptions and are stable. It means in case a large and unpredicted event occurs; the model can provide valuable insight to solutions throughout the event.


  • The major disadvantage is that time series analysis is expensive because forecasts are derived from historical data patterns that are necessary to predict the upcoming market behavior.