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Forecasting the future values based on the given time series data is time series analysis. In time series data, observations are in evenly spaced time intervals like Weeks, Months, Quarters, Years etc.

Some of the examples of time series data are,

  • Yearly Sales Numbers
  • Daily closing price of stocks
  • Monthly Rainfall
  • Budgetary analysis etc.

And Time Series Analysis is used in many applications such as:

  • Sales Forecasting
  • Demand Forecasting
  • Economic Forecasting
  • Budgetary analysis etc.

Time Series can be decomposed into three components,

  • Trend (Upwards/downwards, long term direction)
  • Seasonal: For Eg, Sales of AC in Summers higher than winters
  • Irregularity: Short term Fluctuations.

In this tutorial, will discuss step by step analysis techniques which we use for time series analysis.

Moving Average or Smoothing Techniques

  • Single Moving Average(SMA)
  • Centered Moving Average(CMA)

Exponential Smoothing Techniques

  • Single Exponential Smoothing
  • Forecasting with Single Exponential Smoothing
  • Double Exponential Smoothing
  • Forecasting with Double Exponential Smoothing
  • Triple Exponential Smoothing
  • Example of Triple Exponential Smoothing
  • Exponential Smoothing Summary

Univariate Time Series Models

  • Stationarity
  • Seasonality
  • Common Approaches
  • Box-Jenkins Approach
  • Box-Jenkins Model Identification
  • Box-Jenkins Model Estimation
  • Box-Jenkins Model Validation
  • Example of Univariate Box-Jenkins Analysis
  • Box-Jenkins Model Analysis on Seasonal Data

Multivariate Time Series Models

Example of Multivariate Time Series Analysis.

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