code: def rmsse(test_actual, test_prediction, train_actual):
      """Computes Root Mean Squared Scaled Error (RMSSE)
  
      A variant of the well-known Mean Absolute Scaled Error (MASE) proposed by Hyndman and Koehler (2006)
  
      Intuitively, the general idea is to measure additional value added by the model
      compared to a naive lag-1 predictor.
  
      .. math::
          \sqrt{\frac{1}{h}\frac{\sum^{n+h}_{t=n+1}(Y_t-\hat{Y}_t)^2}{\frac{1}{n-1}\sum^{n}_{t=2}{(Y_t-Y_{t-1})^2}
  
      Notes
      -----
      Reference from https://mofc.unic.ac.cy/m5-guidelines/
  
      """
  
      # exclude leading zeros for training
      first_nz = np.min(np.flatnonzero(train_actual))
      train_actual = test_actual[first_nz:]
  
      lag1_mse = mse(train_actual[1:], train_actual[:-1])
  
      forecast_mse = mse(test_actual, test_prediction)
  
      return np.sqrt(forecast_mse / lag1_mse)
  
file_path: orbit/orbit/diagnostics/metrics.py
imports: import numpy
label_desc: Use image node_id 7 for calling a global function with example usage: rmsse(test_actual, test_prediction, train_actual) and returns: np