Contenu du cours
Time Series Analysis
Time Series Analysis
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():
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():
In the same way, we calculate the error value for the previous model:
The smaller the MSE value, the correspondingly smaller the error.
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 thedfDataFrame. - Fit the
modelto data. - Make predictions using the
model. Start forecasting at the first row (thestartparameter), and set thedynamicparameter toFalse. - Visualize the results: show the first 150 observations of the
"Sales"column of thedfDataFrame within the first call of the.plot()function and the first 150 predicted values within the second call.
Solution
Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessousMerci pour vos commentaires !
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():
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():
In the same way, we calculate the error value for the previous model:
The smaller the MSE value, the correspondingly smaller the error.
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 thedfDataFrame. - Fit the
modelto data. - Make predictions using the
model. Start forecasting at the first row (thestartparameter), and set thedynamicparameter toFalse. - Visualize the results: show the first 150 observations of the
"Sales"column of thedfDataFrame within the first call of the.plot()function and the first 150 predicted values within the second call.
Solution
Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessousMerci pour vos commentaires !
Passez à un bureau pour une pratique réelleContinuez d'où vous êtes en utilisant l'une des options ci-dessous