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.

IBM SPSS Webinar on Youtube

For those who want to catch the webinar on IBM SPSS conducted on Jan 8th, we have uploaded the full 2 hour session on youtube. The audio can be a bit patchy in parts though.

The dataset used for the credit scoring exercise can be downloaded from here. The dataset with the validation report on deciles and cutoffs/profit calculation can be downloaded from here.

Do let us know your feedback in the comments section, for more information you can drop an email at info@learnanalytics.in