Measures of Forecasting Errors

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  1. Supply Chain Analytics
  2. Measures
  3. of
  4. Forecasting
  5. Errors
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The seasonal index for demand data is calculated by following these steps:

Calculate the Average Demand: First, compute the average demand for the year in question. For example, the average demand for 2016 is calculated by summing the monthly demands from January to December and dividing by 12.
Deseasonalize the Demand: To find the seasonal index for each month, divide the actual demand for each month by the average demand of that year. For instance, if the actual demand for January is 19.36 and the average demand for 2016 is 24.07, the seasonal index for January would be {19.36} \ {24.07} =approx. 0.804

aniruddha
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There are three types of exponential smoothing models:

Simple Exponential Smoothing Model: This model is used when there is no trend or seasonality in the data.
Linear Trend Exponential Smoothing Model: This model is applied when there is a linear trend present in the data.
Ratio Seasonality Model: This is a more complex model that accounts for seasonality in the data, specifically when the seasonality is of a ratio type.
These models are part of a broader discussion on how to handle fluctuations in demand data and improve forecasting accuracy.

aniruddha
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How are we selecting the Base value and how are we correcting it from month to month?

davidprotic