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Table 1 Approaches for past correction

From: PreCount: a predictive model for correcting real-time occupancy count data

  Methodology Constraints
Naive approach 1. It subdivides past raw counts into daily profiles with timestamps {t0,…,tn}, where n is the end of the day. It assumes that most buildings have periods during night time where the number of occupants go to zero.
  2. It initializes the first transition of each day i.e. transition at time t0 to zero.  
  3. It assigns zero to all negative counts.  
Probabilistic approach 1. It subdivides past raw counts into daily profiles with timestamps {t0,…,tn}, where n is the end of the day. 1. It requires the observed maximum number of occupants to formulate and compute a transition matrix.
  2. It corrects each daily profile at time tn using specific methods proposed in either Sangoboye and Kjærgaard (2016) and Kuutti et al. (2014). 2. It requires a complete estimation of the transition matrix propagation for each day to accurately correct obtained counts.