code: def forecast(self, ts, nfuture):
          
          jts = _py2java(self._ctx, Vectors.dense(ts))
          jfore = self._jmodel.forecast(jts, nfuture)
          return _java2py(self._ctx, jfore)
docs: Provided fitted values for timeseries ts as 1-step ahead forecasts, based on current
          model parameters, and then provide `nFuture` periods of forecast. We assume AR terms
          prior to the start of the series are equal to the model's intercept term (or 0.0, if fit
          without and intercept term).Meanwhile, MA terms prior to the start are assumed to be 0.0. If
          there is differencing, the first d terms come from the original series.
         
          Parameters
          ----------
          ts:
              Timeseries to use as gold-standard. Each value (i) in the returning series
              is a 1-step ahead forecast of ts(i). We use the difference between ts(i) -
              estimate(i) to calculate the error at time i, which is used for the moving
              average terms. Numpy array.
          nFuture:
              Periods in the future to forecast (beyond length of ts)
              
          Returns a series consisting of fitted 1-step ahead forecasts for historicals and then
          `nFuture` periods of forecasts. Note that in the future values error terms become
          zero and prior predictions are used for any AR terms.