Forecast validation with historical data and seasonalities
The validity of the basis of calculation should be checked for each forecast. This can be done with a past forecast in which the data of the calculation model is checked and compared with the real development.
Why do we need to validate forecast?
The validation of the inventory data involves the determination of a reliable calculation model. A past forecast provides us with the data for this: Validated seasonality data.
In the standard case, we can never derive a uniform calculation model from the entire bandwidth of a data set, which would be the cause for the recurrence of certain patterns.
In order to determine such a calculation model, a realistic seasonality must be calculated for a realistic forecast. Seasonality is a necessary input for regression analysis from a dataset, and describes the period of data collection in points. For example, if the dataset lists 1,000 consecutive days of organic website clicks from Google Search, that would be 1,000 data points. A seasonality could now be set to a specific value to be taken into account. Data points could be measured in terms of years, quarters, thirds, months, weeks, days, hours, minutes and seconds – what matters is that the metric and spacing between entries is the same.
If we want to forecast the development for 6 – 12 months, the seasonality would also have to be measured at 6 – 12 months. In the case of days as data points, this would correspond to 182.5 to 365 points in seasonality. As a best practice, we recommend starting with a seasonality of 200 points for organic website clicks.
Left blue bars: example inventory data for website clicks from organic Google search. On the right in yellow bars: forecast values from inventory data.
Left blue bars: Example inventory data for website clicks from organic Google search. On the right in yellow bars: Real development of the inventory data.
Comparison between real inventory data and forecast values. The decisive factor here is whether the development of the trend is, by and large, realistic.
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