THE KALMAN FILTER MODEL AND BAYESIAN OUTLIER DETECTION FOR TIME-SERIES ANALYSIS OF BOD DATA

The purpose of this paper is to fit a trigonometric time series model to a biochemical oxygen demand (BOD) data set using the Kalman filter approach to allow estimates of the parameters to be updated recursivel y with each new observation. In addition, we analyse the data set for outliers by computing the prior and posterior probabilities for all ob servations. The smoothing equations result in a better fit of the prop osed model than the model of Papadopoulos et al. (Ecological Modelling , 55(1991): 57-65) based on the same data, in the sense that it reduce s the mean squared error by more than half.