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#Create EIA API using your specific API key | |
api_key = 'YOUR API KEY HERE' | |
api = eia.API(api_key) | |
#Pull the oil WTI price data | |
series_ID='PET.RWTC.D' | |
#Retrieve Data By Series ID | |
series_search = api.data_by_series(series=series_ID) | |
##Create a pandas dataframe from the retrieved time series | |
price_df = pd.DataFrame(series_search) | |
price_df.reset_index(level=0, inplace=True) | |
#Rename the columns for easier analysis | |
price_df.rename(columns={'index':'Date', | |
price_df.columns[1]:'WTI_Price'}, | |
inplace=True) | |
#Format the 'Date' column | |
price_df['Date']=price_df['Date'].astype(str).str[:-3] | |
#Convert the Date column into a date object | |
price_df['Date']=pd.to_datetime(price_df['Date'], format='%Y %m%d') | |
#Subset to only include data going back to 2014 | |
price_df=price_df[(price_df['Date']>='2014-01-01')] | |
#Convert the time series values to a numpy 1D array | |
points=np.array(price_df['WTI_Price']) | |
#RUPTURES PACKAGE | |
#Changepoint detection with the Pelt search method | |
model="rbf" | |
algo = rpt.Pelt(model=model).fit(points) | |
result = algo.predict(pen=10) | |
rpt.display(points, result, figsize=(10, 6)) | |
plt.title('Change Point Detection: Pelt Search Method') | |
plt.show() | |
#Changepoint detection with the Binary Segmentation search method | |
model = "l2" | |
algo = rpt.Binseg(model=model).fit(points) | |
my_bkps = algo.predict(n_bkps=10) | |
# show results | |
rpt.show.display(points, my_bkps, figsize=(10, 6)) | |
plt.title('Change Point Detection: Binary Segmentation Search Method') | |
plt.show() | |
#Changepoint detection with window-based search method | |
model = "l2" | |
algo = rpt.Window(width=40, model=model).fit(points) | |
my_bkps = algo.predict(n_bkps=10) | |
rpt.show.display(points, my_bkps, figsize=(10, 6)) | |
plt.title('Change Point Detection: Window-Based Search Method') | |
plt.show() | |
#Changepoint detection with dynamic programming search method | |
model = "l1" | |
algo = rpt.Dynp(model=model, min_size=3, jump=5).fit(points) | |
my_bkps = algo.predict(n_bkps=10) | |
rpt.show.display(points, my_bkps, figsize=(10, 6)) | |
plt.title('Change Point Detection: Dynamic Programming Search Method') | |
plt.show() |
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