Time Series using Holt’s Linear Exponential Smoothing (Seasonal Variation)

In this video , we explain how to implement Exponential Smoothing on Excel itself to generate a forecast.

We begin by explaining the decomposition of time series into 4 components

  • Trend (Long Term Progression of the Series)
  • Seasonality
  • Cyclic
  • Irregular/Noise

We then demonstrate the use of Moving averages and single exponential smoothing to extract the trend from the series. By subtracting trend from the original signal we can extract the seasonal variation around the trend.

Further we demonstrate the Holt’s technique for double exponential smoothing in a linear upwards trend and how we can use it for forecasting. Furthermore, by using the length of the season, we average out the seasonal fluctuation around the trend (thereby try to eliminate the irregular component) and then combine the forecasted trend and seasonal fluctuation to get an integrated forecast.

All of the above has been demonstrated using MS Excel and simple formulae, and then we proceed to demonstrate the use of IBM SPSS to do the same.

The worksheet with the implementation can be downloaded from here.

Comments


One thought on “Time Series using Holt’s Linear Exponential Smoothing (Seasonal Variation)

  1. Hey Karan, this is Arti. Great work by you & Uma. The time series video is awesome.I am actually doing a mini project which requires time series and your video was most helpful.

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