code: def execute(self, timeSeries):
          
          # determine the number of values to forecast, if necessary
          self._calculate_values_to_forecast(timeSeries)
  
          seasonLength = self.get_parameter("seasonLength")
          if len(timeSeries) < seasonLength:
              raise ValueError("The time series must contain at least one full season.")
  
          alpha = self.get_parameter("smoothingFactor")
          beta = self.get_parameter("trendSmoothingFactor")
          gamma = self.get_parameter("seasonSmoothingFactor")
  
  
          seasonValues = self.initSeasonFactors(timeSeries)
          resultList = []
          lastEstimator = 0
  
          for idx in xrange(len(timeSeries)):
              t = timeSeries[idx][0]
              x_t = timeSeries[idx][1]
              if idx == 0:
                  lastTrend = self.initialTrendSmoothingFactors(timeSeries)
                  lastEstimator = x_t
                  resultList.append([t, x_t])
                  continue
  
              lastSeasonValue = seasonValues[idx % seasonLength]
  
              estimator = alpha * x_t/lastSeasonValue + (1 - alpha) * (lastEstimator + lastTrend)
              lastTrend = beta * (estimator - lastEstimator) + (1 - beta) * lastTrend
              seasonValues[idx % seasonLength] = gamma * x_t/estimator + (1 - gamma) * lastSeasonValue
  
              lastEstimator = estimator
              resultList.append([t, estimator])
  
          resultList += self._calculate_forecast(timeSeries, resultList, seasonValues, [lastEstimator, lastSeasonValue, lastTrend])
          return TimeSeries.from_twodim_list(resultList)
docs: Creates a new TimeSeries containing the smoothed values.
  
          :param TimeSeries timeSeries: TimeSeries containing hte data.
  
          :return:    TimeSeries object containing the exponentially smoothed TimeSeries,
              including the forecasted values.
          :rtype:     TimeSeries
  
          :note: Currently the first normalized value is simply chosen as the starting point.