Hi, welcome back.
I work in the previous lecture, we learned about the do basic forecasting techniques, naïf method
and the simple average method.
In this lecture, we are going to learn about the metrics for timecards forecasting.
So let's begin.
You're going to learn about the five most popular evaluation metrics used to evaluate the accuracy and
efficiency of a timecards forecasting first mean forecast error that is MiFi second mean absolute error.
That is M80 could mean absolute percentage error forward mean square error and finally root mean square
error are messy.
There are so many things to learn.
But let's start with mean forecast error.
The mean forecast error shows the deviation of the forecast from the actual demand.
This is the mean of the sum of all the differences between the actual values and forecasted values.
Now consider a case where we have 10 data points and the error for each data points are minus two,
minus four three eight minus three, minus eight one minus one five minus five.
Here, the sum of all the errors come out to be zero.
So according to our evaluation metric, mean for cost at all.
Our models forecast has absolutely correct values.
But do you think this is correct?
Yes, you've got it right.
The answer is no.
Is the evaluation metric giving us the correct answer?
Absolutely not.
As we can see from the error values that there is a much bigger difference between the actual values
and the forecasted values.
In this, we took small one digit numbers, but in cases where larger values are associated, this can
be a huge problem as it is canceling out all the overestimated and underestimated values.
How to deal with this problem for dealing with these kind of problems.
We have another evaluation metric called mean absolute error.
This method is similar to mean forecast error with a slight change.
That is, we will consider the absolute values of the differences between the actual values and the
forecasted values.
But the major problem that we face with the mean absolute error is that it does not give any clear information
about the error.
Value Calculated does not have anything to compare with.
For such a scenario, we can use another evaluation metric called mean absolute percentage error mean
absolute percentage error calculates the percentage of mean absolute error to get a clear idea of how
much the forecasted values deviates from the actual value.
This sounds cool.
Let's also discuss the rest of the evaluation metrics.
The next evaluation metric is mean squared error.
The basic idea behind mean squared error is the same as mean absolute error.
In order to capture deviations, we square the differences, sum them up and take the average.
Since we square the differences, the error value is not of the same dimension as the values.
So we take the square root of the error and name this metric as root mean squared error.
Now you must have got the idea of evaluation metrics used for estimating the quality of forecasting
time TDS data.
In the next lecture, we will learn a few more forecasting techniques and then stay tuned and goodbye.