old_contents: # http://github.com/timestocome
  
  
  import numpy as np 
  import pandas as pd 
  import matplotlib.pyplot as plt 
  from fbprophet import Prophet
  
  
  # https://research.fb.com/prophet-forecasting-at-scale/
  # https://facebookincubator.github.io/prophet/docs/quick_start.html
  
  ###################################################################################
  # read in and set up data
  # read in nasdaq 
  data = pd.read_csv('data/nasdaq.csv', parse_dates=True, index_col='Date')
  data['Open'] = pd.to_numeric(data['Open'], errors='coerce')
  
  # convert to log scale
  data['LogOpen'] = np.log(data[['Open']]) 
  
  # fbProphet expects a date series with the data column named 'y', and the date named 'ds'
  df = pd.DataFrame({'ds':data.index, 'y':data.LogOpen}) 
  
  # check everything looks okay
  print(df)
  
  
  ###################################################################################
  # model 
  
  # create and fit model
  m = Prophet()
  m.fit(df)
  
  # prediction 
  future = m.make_future_dataframe(periods=251)       # 251 ~one year of trading days
  forecast = m.predict(future)
  #print(forecast)
  
  m.plot(forecast)
  plt.title("Nasdaq forecast using FBProphet")
  plt.savefig("Prophet_Nasdaq_forecast.png")
  plt.show()
  
  
  m.plot_components(forecast)
  plt.title('Components of FBProphet Nasdaq Forecast')
  plt.savefig('Prophet_Nasdaq_components.png')
  plt.show()
new_contents: # http://github.com/timestocome
  
  
  import numpy as np 
  import pandas as pd 
  import matplotlib.pyplot as plt 
  from fbprophet import Prophet
  
  
  # https://research.fb.com/prophet-forecasting-at-scale/
  # https://facebookincubator.github.io/prophet/docs/quick_start.html
  # https://facebookincubator.github.io/prophet/static/prophet_paper_20170113.pdf
  
  ###################################################################################
  # read in and set up data
  # read in nasdaq 
  data = pd.read_csv('data/nasdaq.csv', parse_dates=True, index_col='Date')
  data['Open'] = pd.to_numeric(data['Open'], errors='coerce')
  
  # convert to log scale
  data['LogOpen'] = np.log(data[['Open']]) 
  
  # fbProphet expects a date series with the data column named 'y', and the date named 'ds'
  df = pd.DataFrame({'ds':data.index, 'y':data.LogOpen}) 
  
  # check everything looks okay
  print(df)
  
  
  ###################################################################################
  # model 
  
  # create and fit model
  m = Prophet()
  m.fit(df)
  
  # prediction 
  future = m.make_future_dataframe(periods=251)       # 251 ~one year of trading days
  forecast = m.predict(future)
  #print(forecast)
  
  m.plot(forecast)
  plt.title("Nasdaq forecast using FBProphet")
  plt.savefig("Prophet_Nasdaq_forecast.png")
  plt.show()
  
  
  m.plot_components(forecast)
  plt.title('Components of FBProphet Nasdaq Forecast')
  plt.savefig('Prophet_Nasdaq_components.png')
  plt.show()
subject: add link to paper in comments