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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def execute(self, timeSeries):
 """Creates a new TimeSeries containing the smoothed and forcasted values. :return: TimeSeries object containing the smoothed TimeSeries, including the forecasted values. :rtype: TimeSeries :note: The first normalized value is chosen as the starting point. """

        # determine the number of values to forecast, if necessary
        self._calculate_values_to_forecast(timeSeries)

        # extract the required parameters, performance improvement
        alpha            = self._parameters["smoothingFactor"]
        valuesToForecast = self._parameters["valuesToForecast"]

        # initialize some variables
        resultList  = []
        estimator   = None
        lastT       = None

        # "It's always about performance!"
        append = resultList.append

        # smooth the existing TimeSeries data
        for idx in xrange(len(timeSeries)):
            # get the current to increase performance
            t = timeSeries[idx]

            # get the initial estimate
            if estimator is None:
                estimator = t[1]
                continue

            # add the first value to the resultList without any correction
            if 0 == len(resultList):
                append([t[0], estimator])
                lastT = t
                continue

            # calculate the error made during the last estimation
            error = lastT[1] - estimator

            # calculate the new estimator, based on the last occured value, the error and the smoothingFactor
            estimator = estimator + alpha * error

            # save the current value for the next iteration
            lastT = t

            # add an entry to the result
            append([t[0], estimator])

        # forecast additional values if requested
        if valuesToForecast > 0:
            currentTime        = resultList[-1][0]
            normalizedTimeDiff = currentTime - resultList[-2][0]

            for idx in xrange(valuesToForecast):
                currentTime += normalizedTimeDiff

                # reuse everything
                error     = lastT[1] - estimator
                estimator = estimator + alpha * error

                # add a forecasted value
                append([currentTime, estimator])

                # set variables for next iteration
                lastT         = resultList[-1]

        # return a TimeSeries, containing the result
        return TimeSeries.from_twodim_list(resultList)