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In this lecture, we will learn how to implement moving evidence.

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Or am I model in Python?

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In Amman, Jordan, Verein auto regulation on the residual values of over time cities.

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So let's first create the recipe and values.

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We are going to create Tresillian values by implementing nine model first.

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So this is our dataset.

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We are using the same dataset.

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If you remember, we created my forecast, some lectors back.

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We are going to do the same thing here.

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We are creating a new column, which is speech by shifting over temperature, column by one record.

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So this is a leg, one record.

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Now, temp is the value we want to predict.

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And this is the X value.

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So if we just take or friends of them and pee, it will give us the serial values, flawed make model.

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If you are not comfortable with knife model or the computation that we are doing here, I recommend

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you to watch our name more than implementation in Python video first and then watch this video.

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So you have created the vestibules as well.

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So let's look at the first five and use of four data.

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First few of their day.

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These are the actual values.

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These are Democratic trade values, and their difference is that that's a and value.

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So we have implemented my forecast model.

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And we've also calculated great students from that model in our data.

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Known for moving average.

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We will run our total duration on this.

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That's the dual values.

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So let's first create tests and train data.

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We are selecting that third column from our D of data frame.

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And we are selecting last seven rows as our test data and the remaining rules as the train data since

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the first value is.

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And then we are ignoring it and we are saving all the records from the second one due to all the records

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except the last seven records as screen.

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And we are saving seven records as our test data.

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So let's trendies also, let's look at it first by Riluzole four green nitta.

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You can see we have all of these values here.

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And you can see that we have this sets of other values in our crane data.

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No, next, we have to implement auto regulation model on this.

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Rest of the world will lose.

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So we are importing it from its Smardon.

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We are creating a model object using function, and we are giving our train data as an input.

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And the next step, we are putting out more.

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Now, let's look at how many lagging variables are important in this auto regression on that Saleel

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model.

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You can see there won't be line lag where he will study important for this or politician.

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Now, we can also get the coefficient of all this variables.

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So you can see.

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These are the parameters.

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And remember, this Elvan value is the Legba value on Brazilian's.

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These are not the leg, the value of forward time series data.

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This out of the leg, the value of the silhouettes of night forecasts on a lot more.

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So we have coefficient of all the 29 leg variables.

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Now, if we just predict using a predict function of this model.

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We will be predicting the forecast, said glycerol for the next well do in time cities.

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But to get the forecast said value of all time cities, we have to add this forecast, said Tresillian

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in the forecast.

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Say night value that we have Vita's in this column.

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So let's spread the vestibule values.

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We are going to use the same model, underscore the fact, not predict.

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And we have to give a start, point and then point, we are giving the starting point as this Satoh

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forward test that is length of crane and the end point test length of crane plus length of test, minus

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one.

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And this.

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Now we have the forecast said, let's return value.

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The feed just right.

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First,

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you can see that we are getting the seven values this updo forecast said yesterday on the value.

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Now, we have to add this forecast said resolution values in the forecast that we have already made.

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What is the forecast that we have already made?

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That is.

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This so since we are using night forecast, which is stored in Colombe, we are getting the last seven

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records of day.

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You can see this at the forecasted values and these are the forecasted resolutions.

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So if we add this to data frame, we'll get the final forecasted value from our moving average model.

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So this is what we are doing here.

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We are creating another data frame that is predictions and we are adding these two values.

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Let's look at the values of this prediction.

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So these are the forecasted values from what, moving average more than.

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Let's calculate the MASC value, the MSU value is two.

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So if you remember from 079 forecast, we were getting masc value of around three point four.

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From what?

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Here model, we were getting masc value of one point five from over.

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We are more done with walk forward validation.

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We were getting masc value of around one point four and using this and more than we are getting an masc

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value of two.

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We can also lorden the same.

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Using the same port, you can see that this the blue line is the actual blue and red line is a credit

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card value.

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So this is all reimplement memorial.

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And by then I will repeat these steps once more.

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So first you have to use a model to create forecast set values, and then you'll need to create Tresillian

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values on those forecasted values.

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So the use may create the forecasted values.

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And then we.

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And then we took a defense off all night forecast and actual values to clear cultural values.

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We used it more than to train our model on the resolution values and to predict future forecasted reticule

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values.

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Then we again used our future forecast and values of our rules and added a two hour night forecast,

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said value to get the final forecast, said value trauma.

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What am I, modern?

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You can also implement walk forward on a memorial as well.

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You just have to run the same code on resolutions instead of actual values and then use those vestigial

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values to credit the final values.

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I recommend you to do that on your own.

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That's all for this video.

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Thank you.