text: import pandas as pd
  import numpy as np
  
  
  def load_iclaims(end_date='2018-06-24', transform=True):
      """Load iclaims dataset
  
      Returns
      -------
          pd.DataFrame
  
      Notes
      -----
      iclaims is a dataset containing the weekly initial claims for US unemployment benefits against a few related google
      trend queries (unemploy, filling and job)from Jan 2010 - June 2018. This aims to mimick the dataset from the paper
      Predicting the Present with Bayesian Structural Time Series by SCOTT and VARIAN (2014).
      Number of claims are obtained from [Federal Reserve Bank of St. Louis] while google queries are obtained through
      Google Trends API.
  
      Note that dataset is transformed by natural log before fitting in order to be fitted as a multiplicative model.
  
      https://fred.stlouisfed.org/series/ICNSA
      https://trends.google.com/trends/?geo=US
      https://finance.yahoo.com/
      """
      url = 'https://raw.githubusercontent.com/uber/orbit/master/examples/data/iclaims_example.csv'
      df = pd.read_csv(url, parse_dates=['week'])
      df = df[df['week'] <= end_date]
  
      # standardize the regressors by mean; equivalent to subtracting mean after np.log
      regressors = ['trend.unemploy', 'trend.filling', 'trend.job', 'sp500', 'vix']
  
      # convert to float
      for col in regressors:
          df[col] = df[col].astype(float)
  
      if transform:
          # log transfer
          df[['claims'] + regressors] = df[['claims'] + regressors].apply(np.log)
          # de-mean
          df[regressors] = df[regressors] - df[regressors].apply(np.mean)
  
      return df
  
  
  def load_m4weekly():
      """Load m4 weekly sample dataset
  
      Returns
      -------
          pandas DataFrame
  
      Notes
      -----
      https://forecasters.org/resources/time-series-data/m4-competition/
      """
      url = 'https://raw.githubusercontent.com/uber/orbit/master/examples/data/m4_weekly.csv'
      df = pd.read_csv(url, parse_dates=['date'])
  
      return df
  
  
  def load_m5daily():
      """Load m5 aggregated daily dataset
  
      Returns
      -------
          pandas DataFrame
  
      Notes
      -----
      https://www.kaggle.com/c/m5-forecasting-accuracy
      """
      url = 'https://raw.githubusercontent.com/uber/orbit/master/examples/data/m5_agg_demand_full.csv'
      df = pd.read_csv(url, parse_dates=['date'])
  
      return df
  
  
  def load_m3monthly():
      """Load m3 monthly sample dataset
  
      Returns
      -------
          pandas DataFrame
  
      Notes
      -----
      https://forecasters.org/resources/time-series-data/m3-competition/
      """
      url = 'https://raw.githubusercontent.com/uber/orbit/master/examples/data/m3_monthly.csv'
      df = pd.read_csv(url, parse_dates=['date'])
  
      return df
  
  
  def load_electricity_demand():
      """Load Turkish electricity demand daily data from 1 January 2000 to 31 December 2008.
      Returns
      -------
          pandas DataFrame
  
      Notes
      -----
      https://robjhyndman.com/publications/complex-seasonality/
  
      :return:
      """
  
      url = 'https://robjhyndman.com/data/turkey_elec.csv'
      df = pd.read_csv(url, header=None, names=['electricity'])
      df['date'] = pd.date_range(start='1/1/2000', end='31/12/2008', freq='D')
      # re-arrange columns
      df = df[['date', 'electricity']]
  
      return df
  
  
  def load_air_passengers():
      """Load air passengers csv from prophet github
      Returns
      -------
          pandas DataFrame
  
      Notes
      -----
      https://github.com/facebook/prophet/tree/master/examples
  
      :return:
      """
  
      url = 'https://raw.githubusercontent.com/facebook/prophet/master/examples/example_air_passengers.csv'
      df = pd.read_csv(url, parse_dates=['ds'])
  
      return df
  