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Now it is time to learn about auto regression forecasting methodology.

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Keep in mind that this method should be applied to time cities without trend and seasonality.

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So if your cities has trend and seasonality.

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Remove it.

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Using the method stored in the previous sections and then applied this method.

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Now, let's understand the concept behind our Doda direction.

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Outdoor regression is basically a linear regression model in a linear regression model.

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The prediction of output value is based on a linear combination of input values.

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This is an equation of a simple linear regression model.

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Whitehead is equal to be zero plus BE1 Times X1 hit VI hard.

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Is the prediction an X?

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Is the input value Beezy to one to be one of the coefficients which are found my optimizing the model

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on the training data.

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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

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to fit a straight line through these points so that the total value of the errors is minimized.

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This gives us a valuable V0 and BE1.

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Once we have V0 and B1 values, we can no predictive value.

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Why, given the value of X?

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We will cover regulation more extensively in the multivariate prediction section.

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For now, we need to understand this much, only that for a regression model.

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Initially, we will feed a set of X and Y values to our model.

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This data is called the training data because using this data, our software will train the model and

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find the values of V0 and B1.

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Once we have visalo and we win values, we can predict the value of Y for new values of X.

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So how do you does in time for these forecasting, if we want to predict the value of some variable,

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say, sales for time period plus one, we can use the observation of previous time periods, such as

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sales of time period B, B minus one or T minus two and so on.

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So if you want to predict the temperature of tomorrow, you can use the temperature of today or yesterday

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to build a linear model equation.

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And because this regression model uses data from the same input variable at previous times steps, it

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is therefore to us auto regression that is regulation of itself.

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So order regression model is this only we take the cities usually lag values or bring the model.

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Once the model is train, we can predict the future values using it.

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Now, let's look at the practical part.

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You will get more clarity also by implementing this model in parts of the.