  In recent years, there have been many practical applications of anomaly
detection such as in predictive maintenance, detection of credit fraud, network
intrusion, and system failure. The goal of anomaly detection is to identify in
the test data anomalous behaviors that are either rare or unseen in the
training data. This is a common goal in predictive maintenance, which aims to
forecast the imminent faults of an appliance given abundant samples of normal
behaviors. Local outlier factor (LOF) is one of the state-of-the-art models
used for anomaly detection, but the predictive performance of LOF depends
greatly on the selection of hyperparameters. In this paper, we propose a novel,
heuristic methodology to tune the hyperparameters in LOF. A tuned LOF model
that uses the proposed method shows good predictive performance in both
simulations and real data sets.
