Challenge

Oppgave

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Create an autoregressive model to predict the dataset aapl.csv. After, print the results and the model error.

Read the aapl.csv dataset.
Create an autoregressive model (AutoReg) with 3 lags for the X data and assign it to the model variable.
Fit model to the data and assign it to the model_fit variable.
Predict the first 30 values.
Visualize the results: display the first 30 values of X within the first call of the print() function, and first 30 values of the predictions within the second call.
Calculate the RMSE (square root of the mean squared error) and display it.

Løsning

# 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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Seksjon 4. Kapittel 5

# 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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