code: def forecastOneStepAhead(self, df, horizon_index=1, perf_mode=False):
      assert self.mTime in df.columns
      assert self.mOriginalSignal in df.columns
      lPrefix = self.mSignal + "_"
      df1 = df
      # df1.to_csv("before.csv");
      # add new line with new time value, row_number and nromalized time
      # add signal tranformed column
      df1 = self.mTransformation.transformDataset(
          df1, self.mOriginalSignal
      )
      # df1.to_csv("after_transformation.csv");
      # print("Transformation update : " , df1.columns);
  
      df1 = self.mTimeInfo.transformDataset(df1)
      # df1.to_csv("after_time.csv");
      # print("TimeInfo update : " , df1.columns);
      # compute the trend based on the transformed column and compute trend residue
      df1 = self.mTrend.transformDataset(df1)
      # print("Trend update : " , df1.columns);
      # df1.to_csv("after_trend.csv");
      # compute the cycle and its residue based on the trend residue
      df1 = self.mCycle.transformDataset(df1)
      # df1.to_csv("after_cycle.csv");
      # print("Cycle update : " , df1.columns);
      # compute the AR componnet and its residue based on the cycle residue
      df1 = self.mAR.transformDataset(df1, horizon_index)
      # df1.to_csv("after_ar.csv");
      # print("AR update : " , df1.columns);
      # compute the forecast and its residue (forecast = trend  + cycle + AR)
      df2 = df1
      lTrendColumn = df2[self.mTrend.mOutName]
      lCycleColumn = df2[self.mCycle.mOutName]
      lARColumn = df2[self.mAR.mOutName]
      lSignal = df2[self.mSignal]
      if not perf_mode:
          df2[lPrefix + "Trend"] = lTrendColumn
          df2[lPrefix + "Trend_residue"] = lSignal - lTrendColumn
          df2[lPrefix + "Cycle"] = lCycleColumn
          df2[lPrefix + "Cycle_residue"] = (
              df2[lPrefix + "Trend_residue"] - lCycleColumn
          )
          df2[lPrefix + "AR"] = lARColumn
          df2[lPrefix + "AR_residue"] = (
              df2[lPrefix + "Cycle_residue"] - lARColumn
          )
  
      lPrefix2 = str(self.mOriginalSignal) + "_"
      # print("TimeSeriesModel_forecast_invert");
      df2[lPrefix + "TransformedForecast"] = (
          lTrendColumn + lCycleColumn + lARColumn
      )
      df2[lPrefix2 + "Forecast"] = self.mTransformation.invert(
          df2[lPrefix + "TransformedForecast"]
      )
  
      if not perf_mode:
          df2[lPrefix + "TransformedResidue"] = (
              lSignal - df2[lPrefix + "TransformedForecast"]
          )
          lOriginalSignal = df2[self.mOriginalSignal]
          df2[lPrefix2 + "Residue"] = (
              lOriginalSignal - df2[lPrefix2 + "Forecast"]
          )
      df2.reset_index(drop=True, inplace=True)
      return df2
  
imports: import pandas
  import numpy
  from .None import PredictionIntervals
  from .None import Plots
  from .None import Perf
  from .None import Utils
label_desc: Use image node_id 9 for calling the cTimeSeriesModel obj's underlying member method code with example usage: obj.forecastOneStepAhead(df, horizon_index, perf_mode) and returns: df2