Lab 1: Welcome to Time Series Analysis - List out all the values.
Time Series Analysis - List out all the values.
Solution 1:
# Task 1: Run the below to import the data for this hands-on, # The data contains All stocks in the S-P 500 index, and their historical prices for the year 2011 import pandas as pd dataFrame = pd.read_csv('dow_jones_index.data',parse_dates=["date"], index_col="date") dataFrame.head() # Task 2: Run the below cell to filter the closing price related to stock 'AA' closeTS = dataFrame[(dataFrame.stock == 'AA')].close.str.replace('$',' ').astype(float) #print(closeTS) # Task: 3 Using the data filtered in the above step list all the closing price from Jan to March, # assign the resulting series to variable 'close_AA' ### Start code here close_AA = closeTS.loc['2011-01-01': '2011-03-31'] ### End code(approx 1 line) # Task 4: Run the below cells to save your answers from test_ts_listvalues import values values.save_ans1(close_AA)
Lab 2: Welcome to Time Series Analysis - Resampling month wise.
Time Series - Resampling month wise.
Solution 2:
# Task 1: Run the below to import the data for this hands-on, # The data contains All stocks in the S-P 500 index, and their historical prices for the year 2011 import pandas as pd dataFrame = pd.read_csv('dow_jones_index.data',parse_dates=["date"], index_col="date") dataFrame.head() # Task 2: Run the below cell to filter the closing price related to stock 'AA' closeTS = dataFrame[(dataFrame.stock == 'AA')].close.str.replace('$',' ').astype(float) #print(closeTS) # Task 3: Downsample the data filtered in the above step month wise and fill the max value of closing price for each month # assign the resulting series to variable 'downsample' ###Start code here downsample = closeTS.resample('M').max() #print(downsample) ###End code(approx 1 line) # Task 4: Run the below cells to save your answers from test_ts_resample_month import values values.save_ans1(downsample)
Lab 3: Welcome to Time Series Analysis - Resampling day wise and Interpolate
Time series - Resampling day wise and Interpolate
Solution 3:
# Task 1: Run the below to import the data for this hands-on, # The data contains All stocks in the S-P 500 index, and their historical prices for the year 2011 import pandas as pd dataFrame = pd.read_csv('dow_jones_index.data',parse_dates=["date"], index_col="date") dataFrame.head() # Task 2: Run the below cell to filter the closing price related to stock 'AA' closeTS = dataFrame[(dataFrame.stock == 'AA')].close.str.replace('$',' ').astype(float) #print(closeTS) # Task 3: upsample the data filtered in the above step day wise and perform interpolation to forward fill the first two 'Nan' values. # return the first 10 samples of upsampled data to variable 'upsample' ###Start code here upsample = closeTS.resample('D' , fill_method='ffill', limit=2).head(10) #print(upsample) ###End code(approx 1 line) # Task 4: Run the below cells to save your answers from test_ts_resample_month import values values.save_ans1(upsample)
Lab 4: Time series - WMT stock
Welcome to Time Series Analysis - WMT stock
Solution 4:
# Task 1 # Run the below to import the data for this hands-on # The data contains all stocks in the S&P 500 index, and their historical prices for the year 2011 # Dataset : http://archive.ics.uci.edu/ml/machine-learning-databases/00312/dow_jones_index.zip import pandas as pd dataFrame = pd.read_csv('dow_jones_index.data',parse_dates=["date"], index_col="date") dataFrame.head() # Task 2: # Run the below cell to filter the opening price related to stock 'WMT'.The sample is returned to variable 'open_WMT_Ts' open_WMT_Ts = dataFrame[(dataFrame.stock == 'WMT')].open.str.replace('$',' ').astype(float) open_WMT_Ts.head() # Task 3: # from statsmodels import adfuller method ###Start code here from statsmodels.tsa.stattools import adfuller ###End code # Task 4: # perform stationarity check on WMT opening price using adfuller method and return the result to variable 'tsResult' ###Start code here tsResult = adfuller(open_WMT_Ts) ###End code # Task 5: Run the Cell. print('ADF Statistic: %f' % tsResult[0]) print('p-value: %f' % tsResult[1]) for key, value in tsResult[4].items(): print('\t%s: %.3f' % (key, value)) # Task 6: # Find the value of ADF Statistic from the above test result and assign it to variable ADF_stat ADF_stat = float(tsResult[0]) type(ADF_stat) # Task 7: # Run the below cells to save your answers from test_ts_wmt import values values.save_ans1(ADF_stat)
Lab 5: Welcome to Time Series Analysis - XOM stock
Time series - XOM stock
Solution 5: Time series - XOM stock
# Task 1 # Run the below to import the data for this hands-on # The data contains all stocks in the S&P 500 index, and their historical prices for the year 2011 # Dataset : http://archive.ics.uci.edu/ml/machine-learning-databases/00312/dow_jones_index.zip import pandas as pd dataFrame = pd.read_csv('dow_jones_index.data',parse_dates=["date"], index_col="date") dataFrame.head() # Task 2: # Run the below cell to filter the closing price related to stock 'XOM'. The sample is returned to variable 'close_XOM_Ts' close_XOM_Ts = dataFrame[(dataFrame.stock == 'XOM')].close.str.replace('$',' ').astype(float) close_XOM_Ts.head() # Task 3: # from statsmodels import adfuller method ###Start code here from statsmodels.tsa.stattools import adfuller ###End code # Task 4: # perform stationarity check on XOM closing price using adfuller method and return the result to variable 'tsResult' ###Start code here tsResult = adfuller(close_XOM_Ts) ###End code # Task 5: Run the Cell. print('ADF Statistic: %f' % tsResult[0]) print('p-value: %f' % tsResult[1]) for key, value in tsResult[4].items(): print('\t%s: %.3f' % (key, value)) # Task 6: # Find the value of ADF Statistic from the above test result and assign it to variable ADF_stat ADF_stat = float(tsResult[0]) type(ADF_stat) # Task 7: # Run the below cells to save your answers from test_ts_wmt import values values.save_ans1(ADF_stat)