code: def compute_forecast(self, df, alpha, method, horizon_index=1):
      # print(df.shape)
      # print(df.columns)
      # print(df[['Date', 'Signal', '_Signal', 'row_number', '_Signal_ConstantTrend_residue_zeroCycle_residue']].tail(12))
      lCounts_df = df[[self.mTime, self.mCycleResidueName]].copy()
      counts = lCounts_df[self.mCycleResidueName] - self.mOffset
      counts = counts[:-(horizon_index)]
      # print(list(counts.unique()))
      # print(counts.describe())
      # assert(not np.isnan(counts[:-1]).any())
      #  q is often called the “demand” and a the “inter-arrival time”.
      q = counts[abs(counts) > 1e-8]
      demand_times = pd.Series(list(q.index)) + 1
      a = demand_times - demand_times.shift(1).fillna(0.0)
      df2 = pd.DataFrame(
          {
              "demand_time": list(demand_times),
              "q": list(q),
              "a": list(a),
          }
      )
  
      df2["q_est"] = None
      df2["a_est"] = None
  
      # initialization : first values
      df2.loc[0, "q_est"] = df2["q"][0]
      df2.loc[0, "a_est"] = df2["a"][0]
      for i in range(df2.shape[0] - 1):
          q1 = (1.0 - alpha) * df2["q_est"][i] + alpha * df2["q"][i]
          a1 = (1.0 - alpha) * df2["a_est"][i] + alpha * df2["a"][i]
          df2.loc[i + 1, "q_est"] = q1
          df2.loc[i + 1, "a_est"] = a1
  
      df2["forecast"] = (
          self.get_coeff(alpha, method) * df2["q_est"] / df2["a_est"]
      )
      df2["index"] = df2["demand_time"] - 1
  
      for h in range(horizon_index):
          lCounts_df.loc[-(h + 1), self.mCycleResidueName] = None
      df1 = lCounts_df.reset_index()
      df3 = df1.merge(df2, how="left", on=("index", "index"))
      df4 = df3.fillna(method="ffill")
      # fill first empty fit data with zero counts (when signal starts with zeros)
      i = 0
      while np.isnan(df4.loc[i, "forecast"]):
          df4.loc[i, "forecast"] = 0.0
          i = i + 1
      df4["forecast"] = df4["forecast"] + self.mOffset
      return df4
  
file_path: atspy/atspy/TS/Intermittent_Models.py
imports: import numpy
  import pandas
  from .None import SignalDecomposition_AR
  from .None import Utils
  from .None import Perf
  import sys
label_desc: Use image node_id 7 for calling the cCroston_Model obj's underlying member method code with example usage: obj.compute_forecast(df, alpha, method, horizon_index) and returns: df4