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.

Demo on Time Series using Exponential Smoothing (IBM SPSS and Excel)

Following up on our last week’s Webex session on Logistic Regression for credit scoring (you can catch it here), this Sunday we will demonstrate the technique of exponential smoothing in time series forecasting.

More specifically, during the webinar we will take you through the basic decomposition of a time series into its components:

  • Trend
  • Seasonality
  • Cyclic
  • Error

We will concentrate on extracting and forecasting the trend and seasonality of a series. Trend component can be extracted using Exponential smoothing (Single, double and triple depending on the slope and pattern) and building a seasonal index to forecast the seasonal variation.

The webinar will demonstrate how this can be done using simple formulas on an excel sheet itself and then introduce the time series function in IBM SPSS. A basic introduction to Box-Jenkins (ARIMA) modelling will also be covered.

A trial version of IBM SPSS version 20 can be downloaded from here. (You will need to fill in some details). Those interested in attending the seminar can drop an email at info@learnanalytics.in or fill in the form at http://www.learnanalytics.in/contact.html  and we will mail you the webex invite. The webinar is scheduled on Sunday, 15th Jan, 0930 IST – 0400 GMT.

The webinar is free to attend.

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