Challenge 1

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Time for new challenges! Here is the first challenge, the idea of which is to process the pr_HH Spot Price.csv dataset to turn it from non-stationary to stationary:

Read the dataset.
Test for data stationarity (use adfuller) and display results.
Visualize the initial values of the "Price" column.
Transform data (the "Price" column of the df DataFrame) from non-stationary to stationary using the difference method (using the .diff() method with periods = 1 parameter). Drop NA values. Assign the result to the new_diff variable.
Visualize the modified data (new_diff).
Rerun the ADF test for updated data (new_diff).

Lösning

# Importing libraries

import pandas as pd

import matplotlib.pyplot as plt

from statsmodels.tsa.stattools import adfuller

# Reading dataset

df = pd.read_csv("https://codefinity-content-media.s3.eu-west-1.amazonaws.com/943e906e-4de6-4694-a1df-313ceed7cfe7/pr_HH+Spot+Price.csv")

# Make ADF Test

result = adfuller(df['Price'].values, autolag="AIC")

print("ADF Statistic: %f" % (result[0]))

print("p-value: %f" % (result[1]))

# Visualizing dataset

plt.plot(df["Price"])

plt.show()

# Use differencing method

new_diff = df["Price"].diff(periods=1).dropna()

# Plot stationary data

plt.plot(new_diff)

plt.show()

# Remake ADF test

new_result = adfuller(new_diff.values, autolag="AIC")

print("ADF Statistic: %f" % (new_result[0]))

print("p-value: %f" % (new_result[1]))

Var allt tydligt?

Tack för dina kommentarer!

Avsnitt 5. Kapitel 3

# Importing libraries

import pandas as pd

import matplotlib.pyplot as plt

from statsmodels.tsa.stattools import adfuller

# Reading dataset

df = pd.___("https://codefinity-content-media.s3.eu-west-1.amazonaws.com/943e906e-4de6-4694-a1df-313ceed7cfe7/pr_HH+Spot+Price.csv")

# Make ADF Test

result = ___(df["___"].values, autolag="AIC")

print("ADF Statistic: %f" % (result[0]))

print("p-value: %f" % (result[1]))

# Visualizing dataset

plt.___(___)

plt.___()

# Use differencing method

new_diff = df["___"].___(periods=___).___()

# Plot stationary data

plt.plot(___)

plt.show()

# Remake ADF test

new_result = adfuller(___.values, autolag="AIC")

print("ADF Statistic: %f" % (new_result[0]))

print("p-value: %f" % (new_result[1]))

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