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Table 3 Relevant configuration parameters of the compared methods

From: Anomaly detection in quasi-periodic energy consumption data series: a comparison of algorithms

Algorithm

Configuration parameters

Basic Statistics

Pearson product-momentum correlation coefficient minimum value (0.2)

AR

p order in [25, 305]

ARIMA

p order in [25, 305], d = 1, q = 0

LOF

number of neighbours in [1, 300]

OC SVM

gamma in [0.001, 0.9726], tol in [\(10^{-10}\), 0.1], nu in [0.001, 0.5]

ISOF

number of trees in [20, 200], max samples in [150, 400]

GRU

2 GRU layers both with 32 units. Training: 500 epochs with patience = 30 and batch size = 64.

LSTM

2 LSTM layers both with 32 units. Training: 500 epochs with patience = 30 and batch size = 64.

GRU-MS

2 GRU layers with 64 and 32 units, and 10 units for the output layer. Training: 500 epochs with patience = 30 a batch size = 64.

LSTM-MS

2 LSTM layers with 64 and 32 units, and 10 units for the output layer. Training: 500 epochs with patience = 30 and batch size = 64.

GRU-AE

2 GRU layers with 128 and 64 units. Training: 500 epochs with patience = 30 and batch size = 64.

LSTM-AE

2 LSTM layers with 128 and 64 units. Training: 500 epochs with patience = 30 and batch size = 64.