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In this lecture, we are going to learn how to implement, walk forward validation for any of the time

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series technique.

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So here we will implement, walk forward validation for auto regression model.

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This is what we are going to do.

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We will run a for loop for all the values of test.

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So suppose this is our test sites.

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We have a test size of five units.

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We will create individual Morton for each of the test value prediction.

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Then in the next step of fall, Lou, we will increase our training dataset to include the new information.

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Then we will again create a new model and forecast for a one time period ahead in the next loop.

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We will again include the additional data and the word training data and create a new model.

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And then again, forecast for one time period ahead.

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We will do this for any number of things where and this the number of test values.

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So if we want to predict five best values, we will run this loop for five times.

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If we want to predict six values.

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We will run this low for six times.

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So let's look at a word for loop one small.

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Before that, I have loaded my data into beer and I have created a sentry inside that set consists of

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seven values.

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So this is what we are going to do.

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We are creating a new dataset that is data.

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This is the data set in which we are going to add the word test values for future predictions.

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So we are initiating this data as data and we are also creating a blank list with the name of credit

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in this list.

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We will install the predicted values of by.

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So here I am initiating the for loop we are using for B in test.

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So suppose if my test, if someone will use this for loop, will run four to seven times, if my test

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has five values, this one loop will run for five things.

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So let's analyze the first run for the first run.

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My data is equal to cream, will create a model object with auto regulation on our data.

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Again, since this is the first in my data is required to train.

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And then we had Pettingill more than using Mordialloc it.

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Then we are predicting the VI with a start tequilla full length of data and and equate to land of green

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plus lente of test minus one.

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This is similar to what we did for auto regression model.

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So here and the first then my wife will consist of seven values.

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Since my Astarte is the start of test and the end of tests.

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Some way here will consist of seven values in the first one.

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Next, we are picking the first value of this.

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Why did the frame?

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And then we are displaying that value and we are also appending that value in the word predict the duffing.

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So this will be my first value.

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If this is the first one, then in the input offer.

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What model?

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Which is data.

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I am finding the value of test data.

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So only I suppose my dream data or data had hundred records at the end of this loop.

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My dream data that this data will have.

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Hundred and one records.

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So we are including this amount of data.

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And do what?

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Training data.

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Now, let's analyze the second loop.

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So now my data have all littering data, plus the first value of test data.

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No, I'm creating my model on that data.

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Now we are predicting why using landform data and then the endpoint.

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So this time my very variable will contain six values.

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We have already predicted one way.

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Forty plus one.

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So now this time, the Wyvill consists value from P plus two, two peoples seven.

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Then again, we are picking the first value of this predicted way and we are also finding this value

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and do not predict list.

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So now my prediction list at the end of second run for loop will contain P plus one by value, which

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was coming from the Force10 and Peoples through a loop, which is coming from the second grade.

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And again, I am appending the test value into my dataset.

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We will run this same sequence 47 times and at the end of this we will never predict list which will

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contain the Vork forward validation y values.

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So let's run this.

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You can see we are getting this.

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Lose US output, as we have also mentioned, the print segment in between.

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If we look at the prediction, this will also contain in the same values.

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You can see we have a list of seven values which we got from the walk forward validation.

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Now, let's look at the masc value of these validations.

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So earlier, if you remember four auto regression, we were getting an MNC value of one point five.

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Now, let's see.

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What value we are going to get from walk forward validation?

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You can see the MASC value is one point forty five.

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So we have decreased our edit by using the walk forward validation.

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If you compare it with knife forecast, in my forecast, we were getting masc value off more than three.

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So this is the significant reduction.

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And you can also see that we are getting improved accuracy using walk forward validation as compared

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to our normal single model for all the values.

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Now let's load this on graph as well.

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You can see we have actual values in blue and the predicted values in red.

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So MSU, when you concluded from work for validation, is much more reliable metrics of evaluating model

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performance.

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And we should always select Morton, which is giving us better accuracy on walk forward validation instead

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of a single model validation.

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So that's all we should do.

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Walk forward validation.

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Four time series data.

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You can see that we can use this method for other models as well.

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So if we are applying moving average or Atima, we can just edit this model fit and model part to implement

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walk forward validation.

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Thank you.