It is near impossible to calculate the accurate forecast despite the availability of multiple forecasting methods. And it is recommended that we should run various forecast models and compare them to find out which model is giving us a more accurate forecast. Let's dive into basics Forecast accuracy
It is near impossible to calculate the accurate forecast despite the availability of multiple forecasting methods. And it is recommended that we should run various forecast models and compare them to find out which model is giving us a more accurate forecast.
For measuring accuracy, we compare the existing data with the data obtained by running the prediction model for existing periods. The difference between the actual and predicted value is also known as forecast error. Lesser the forecast error, the more accurate our model is.
By selecting the method that is most accurate for the data already known, it increases the probability of accurate future values.
There are several measures to measure forecast accuracy:
· Mean Forecast Error (MFE)
· Mean Absolute Error (MAE) or Mean Absolute Deviation (MAD)
· Root Mean Square Error (RMSE)
· Mean Absolute Percentage Error (MAPE)
Let us consider the following table for this example.
The table shows the weekly sales volume of a store. The store manager is not savvy with forecasting models, so he considered the current week’s sales as a forecast for next week. This methodology is also known as the _naïve forecasting method _due to the nature of simplicity.
The difference between the actual value and the forecasted value is known as forecast error. So, in this example, forecast error for week 2 is
Forecast Error (week 2) = 21–17 = 4
A positive value of forecast error signifies that the model has underestimated the actual value of the period. A negative value of forecast error signifies that the model has overestimated the actual value of the period.
The following table calculates the forecast error for the rest of the weeks:
Data Science for Business Users. This tutorial was created to democratize data science for business users (i.e., minimize usage of advanced mathematics topics) and alleviate personal frustration we have experienced on following tutorials and struggling to apply that same tutorial for our needs
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