brockmanmatt

fig, axs = plt.subplots(1, 5, sharey=True, gridspec_kw={'wspace': 0})

fig.set_size_inches(16,6)

x = y = 0

for issue in myCountries:
  train_l = len(time_series)-5

brockmanmatt

fig, axs = plt.subplots(4, 5, sharex=True)

fig.set_size_inches(16,12)

x = y = 0

for issue in time_series:
  train_l = len(time_series)-5

  selected_series = time_series[[col for col in time_series.columns if (col.find(issue[:issue.find("_")]) > -1)]]

brockmanmatt

fig, axs = plt.subplots(4, 5, sharex=True)

fig.set_size_inches(16,12)

x = y = 0

for issue in time_series:

  if not issue.find(".com") > -1:
    continue

brockmanmatt

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

!pip install gdelt
import gdelt
gd = gdelt.gdelt(version=1)

import os
os.makedirs("data",exist_ok=True)

brockmanmatt

mape_df = pd.DataFrame()

fig, axs = plt.subplots(4, 5, sharex=True)

fig.set_size_inches(16,12)

x = y = 0

for issue in time_series:

brockmanmatt

fig, axs = plt.subplots(4, 5, sharex=True)

fig.set_size_inches(16,12)

x = y = 0

for issue in time_series:
  if not issue.find(".com") > -1:
    continue
  train_l = len(time_series)-5

brockmanmatt

!pip install gdelt #make sure gdelt installed
import pandas as pd, numpy as np, matplotlib.pyplot as plt, gdelt, os, datetime, warnings #imports
gd = gdelt.gdelt(version=1) #instantiate object to pull gdelt files

os.makedirs("data",exist_ok=True) #check if there's a data folder
 
cur_date = datetime.datetime(2019,10,7)-datetime.timedelta(days=60) #start pulling from 60 days prior to 10/7

while cur_date < datetime.datetime(2019,10,7): #pull until 10/7
  if not os.path.exists("data/%s-%s-%s.pkl"%(cur_date.year, cur_date.month, cur_date.day)): #if don't have

brockmanmatt

mySources = ["cnn.com", "washingtonpost.com", "nytimes.com", "foxnews.com"]

#unnest the entries with multiple sources in them
df = df.set_index(df.columns.drop('SOURCES',1).tolist()).SOURCES.str.split(';', expand=True).stack().reset_index().rename(columns={0:'SOURCES'}).loc[:, df.columns]

df.DATE = df.DATE.apply(lambda x: str(x)) #convert date
df.DATE = pd.to_datetime(df.DATE)
df.fillna("", inplace=True)
df.set_index("DATE", drop=True, inplace=True)

brockmanmatt

from statsmodels.tsa.statespace.sarimax import SARIMAX

def gen_SARIMA_result(p,d,q, df, issue, test_length): #takes pdq, data, issue to use

  s_model = SARIMAX(endog = df[issue][:-test_length],
                    exog = df[[x for x in df.columns if x != issue]][:-test_length],
                    order=(p,d,q), seasonal_order=(1,0,1,7)).fit()

  f_ru = df[[issue]].copy()[1:] #haven't bothered to change this, but it's the results

brockmanmatt

fig, axs = plt.subplots(1,3, figsize=(12,8))

issue = time_series.columns[0]
test_length = 10
selected_series = time_series[[col for col in time_series.columns if (col.find(issue[issue.find("_"):]) > -1)]].shift()[1:].drop(columns=issue).add_suffix("_l1")
pub_series = time_series[[col for col in time_series.columns if (col.find(issue[:issue.find("_")]) > -1)]].drop(columns=issue).shift()[1:].add_suffix("_l1")
selected_series = selected_series.join(pub_series).join(time_series[issue])
x = 0

for p in [1,5,10]: