Time Series Analysis for Data Insights


Time series analysis is a specialized method for studying a sequence of data points gathered over a time span. Unlike random recording, analysts collect data at consistent intervals over a defined period. To ensure reliability, this analysis usually necessitates a substantial number of data points. Its primary application lies in forecasting, where future data is predicted based on historical data.​

Table of content


  • Why its’ used
  • Data Classifications
  • Types
  • Methods
  • Models
  • Application
  • Conclusion

Why It's used?

Time series analysis aids organizations in comprehending the root causes of trends or consistent patterns over time. Through data visualizations, business users can observe seasonal trends and delve into the reasons behind these patterns.

Time series forecasting is an integral aspect of predictive analytics. It reveals probable alterations in the data, such as seasonality or cyclic behavior, offering an enhanced understanding of data variables and improving the accuracy of forecasts.​

Data classification

Data variations

  • Functional analysis
  • Trend analysis
  • Seasonal variation 

Time Series Analysis Types

  • Descriptive analysis
  • Explanative analysis
  • Exploratory analysis
  • Forecasting
  • Intervention analysis
  • Segmentation

Methods of Time Series Analysis

1-Descriptive Analysis

Visualization Techniques

Summary Statistics

Time Series Decomposition

2-Trend Component

 Seasonal Component

 Cyclic Component

 Irregular or Random     Component

Time Series Analysis Models

  • Autoregressive Integrated Moving Average
  • Seasonal Decomposition of Time Series
  • Exponential Smoothing
  • Seasonal Autoregressive Integrated Moving Average

Application in the recruitment field

  • Understanding Hiring Trends and Patterns
  • Forecasting Recruitment Needs
  • Optimizing Hiring Processes
  • Evaluating Recruitment Strategies​​


Time series analysis models and techniques offer a wide array of tools to capture, model, and forecast patterns within sequential data. From traditional statistical models  to advanced machine learning techniques, selecting the appropriate model requires consideration of data characteristics, complexity, and forecasting requirements.


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