Now it is time to learn about auto regression forecasting methodology.
Keep in mind that this method should be applied to time cities without trend and seasonality.
So if your cities has trend and seasonality.
Remove it.
Using the method stored in the previous sections and then applied this method.
Now, let's understand the concept behind our Doda direction.
Outdoor regression is basically a linear regression model in a linear regression model.
The prediction of output value is based on a linear combination of input values.
This is an equation of a simple linear regression model.
Whitehead is equal to be zero plus BE1 Times X1 hit VI hard.
Is the prediction an X?
Is the input value Beezy to one to be one of the coefficients which are found my optimizing the model
on the training data.
So what we do is this we take a set of X and Y values and feed those to our model, our model then tries
to fit a straight line through these points so that the total value of the errors is minimized.
This gives us a valuable V0 and BE1.
Once we have V0 and B1 values, we can no predictive value.
Why, given the value of X?
We will cover regulation more extensively in the multivariate prediction section.
For now, we need to understand this much, only that for a regression model.
Initially, we will feed a set of X and Y values to our model.
This data is called the training data because using this data, our software will train the model and
find the values of V0 and B1.
Once we have visalo and we win values, we can predict the value of Y for new values of X.
So how do you does in time for these forecasting, if we want to predict the value of some variable,
say, sales for time period plus one, we can use the observation of previous time periods, such as
sales of time period B, B minus one or T minus two and so on.
So if you want to predict the temperature of tomorrow, you can use the temperature of today or yesterday
to build a linear model equation.
And because this regression model uses data from the same input variable at previous times steps, it
is therefore to us auto regression that is regulation of itself.
So order regression model is this only we take the cities usually lag values or bring the model.
Once the model is train, we can predict the future values using it.
Now, let's look at the practical part.
You will get more clarity also by implementing this model in parts of the.