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Autoregression

Let's move on to the review of the autoregressive model:

The formula is similar to the linear regression formula, which is where the name comes from. Instead of the coefficient - the past value of x is used.

With statsmodels we can run an autoregressive model AutoReg():

python

If you notice, the predictions made by the autoregressive model are more accurate than those of the simple moving average.

Let's learn how to evaluate the received results of the trained models. The error is calculated using the mean-squared error. This is done simply with the help of functions sqrt() and mean_squared_error():

python

In the same way, we calculate the error value for the previous model:

The smaller the MSE value, the correspondingly smaller the error.

Uppgift

Swipe to start coding

Create an autoregressive model and train it on the dataset shampoo.csv.

Create an autoregression model (Autoreg) with 6 lags for the "Sales" column of the df DataFrame.
Fit the model to data.
Make predictions using the model. Start forecasting at the first row (the start parameter), and set the dynamic parameter to False.
Visualize the results: show the first 150 observations of the "Sales" column of the df DataFrame within the first call of the .plot() function and the first 150 predicted values within the second call.

Lösning

Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan

Var allt tydligt?

Tack för dina kommentarer!

Avsnitt 4. Kapitel 3

Fråga AI

Fråga vad du vill eller prova någon av de föreslagna frågorna för att starta vårt samtal

Autoregression

Let's move on to the review of the autoregressive model:

The formula is similar to the linear regression formula, which is where the name comes from. Instead of the coefficient - the past value of x is used.

With statsmodels we can run an autoregressive model AutoReg():

python

If you notice, the predictions made by the autoregressive model are more accurate than those of the simple moving average.

python

In the same way, we calculate the error value for the previous model:

The smaller the MSE value, the correspondingly smaller the error.

Uppgift

Swipe to start coding

Create an autoregressive model and train it on the dataset shampoo.csv.

Create an autoregression model (Autoreg) with 6 lags for the "Sales" column of the df DataFrame.
Fit the model to data.
Make predictions using the model. Start forecasting at the first row (the start parameter), and set the dynamic parameter to False.
Visualize the results: show the first 150 observations of the "Sales" column of the df DataFrame within the first call of the .plot() function and the first 150 predicted values within the second call.

Lösning

Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan

Var allt tydligt?

Tack för dina kommentarer!

Avsnitt 4. Kapitel 3

Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan