text: import pandas as pd
  import numpy as np
  
  import pyaf.ForecastEngine as autof
  import pyaf.Bench.TS_datasets as tsds
  
  #get_ipython().magic('matplotlib inline')
  
  b1 = tsds.generate_random_TS(N = 320 , FREQ = 'D', seed = 0, trendtype = "constant", cycle_length = 0, transform = "None", sigma = 0.0);
  df = b1.mPastData
  
  #df.tail(10)
  #df[:-10].tail()
  #df[:-10:-1]
  #df.describe()
  
  
  lEngine = autof.cForecastEngine()
  lEngine
  
  H = b1.mHorizon[b1.mSignalVar];
  lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H);
  lEngine.getModelInfo();
  
  lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
  
  dfapp_in = df.copy();
  dfapp_in.tail()
  
  #H = 12
  dfapp_out = lEngine.forecast(dfapp_in, H);
  dfapp_out.tail(2 * H)
  print("Forecast Columns " , dfapp_out.columns);
  Forecast_DF = dfapp_out[[b1.mTimeVar , b1.mSignalVar, b1.mSignalVar + '_Forecast']]
  print(Forecast_DF.info())
  print("Forecasts\n" , Forecast_DF.tail(H).values);
  
  print("\n\n<ModelInfo>")
  print(lEngine.to_json());
  print("</ModelInfo>\n\n")
  print("\n\n<Forecast>")
  print(Forecast_DF.tail(2*H).to_json(date_format='iso'))
  print("</Forecast>\n\n")
  