Challenge
Tâche
Swipe to start coding
Create an autoregressive model to predict the dataset aapl.csv. After, print the results and the model error.
- Read the
aapl.csvdataset. - Create an autoregressive model (
AutoReg) with 3 lags for theXdata and assign it to themodelvariable. - Fit model to the data and assign it to the
model_fitvariable. - Predict the first 30 values.
- Visualize the results: display the first 30 values of
Xwithin the first call of theprint()function, and first 30 values of thepredictionswithin the second call. - Calculate the RMSE (square root of the mean squared error) and display it.
Solution
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# Importing libraries
from statsmodels.tsa.ar_model import AutoReg
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
from math import sqrt
import pandas as pd
# Read dataset and use "Open" column as X
df = pd.read_csv("https://codefinity-content-media.s3.eu-west-1.amazonaws.com/943e906e-4de6-4694-a1df-313ceed7cfe7/aapl.csv")
X = df["Open"].values
# Train autoregression (use lags=3)
model = AutoReg(X, lags=3, old_names=False)
model_fit = model.fit()
# Make predictions
predictions = model_fit.predict(start=0, end=30, dynamic=False)
# Visualize results
plt.plot(X[:30])
plt.plot(predictions[:30], color="red")
plt.show()
# Calculate MSE error
rmse = sqrt(mean_squared_error(X[:28], predictions))
print("Test RMSE: %.3f" % rmse)
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Section 4. Chapitre 5
single
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# Importing libraries
from statsmodels.tsa.ar_model import AutoReg
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
from math import sqrt
import pandas as pd
# Read dataset and use "Open" column as X
df = pd.___("https://codefinity-content-media.s3.eu-west-1.amazonaws.com/943e906e-4de6-4694-a1df-313ceed7cfe7/aapl.csv")
X = df["Open"].values
# Train autoregression (use lags=3)
model = ___(___, lags=___, old_names=False)
model_fit = ___.___()
# Make predictions
predictions = model_fit.___(start=___, end=___, dynamic=False)
# Visualize results
plt.plot(___[:30])
plt.plot(___[:30], color="red")
plt.show()
# Calculate MSE error
rmse = ___(___(X[:28], predictions))
print("Test RMSE: %.3f" % rmse)
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