Table 3 Types of methods addressing error detection and quantification (RQ2) only, along with its addressed errors, respective papers, and the total number of papers that proposed that method

Principal component analysis

Outliers, bias, drift, stuck-at-zero

[27, 32, 47, 48, 50, 59, 69]

7

Artificial neural network

Outliers, bias, drift, constant values, noise, stuck-at-zero, uncertainty

[34, 36, 54, 70, 78, 81]

6

Ensemble classifiers

Outliers, drift, constant values, noise, uncertainty

[33, 35, 37, 79]

4

Support vector machine

Outliers

[57, 58]

2

Clustering

Outliers

[39, 45]

2

Ontology/knowledge-based systems

Uncertainty (inaccurate data), missing data (incomplete data)

[25, 26]

2

Univariate autoregressive models

Outliers

[40]

1

Statistical generative models

Outliers

[49]

1

Grey prediction model

Outliers, noise, constant values

[52]

1

Particle filtering

Bias, scaling

[71]

1

Association rule mining

Outliers

[56]

1

Bayesian network

Outliers, noise

[44]

1

Euclidean distance

Outliers

[42]

1

Hybrid methods

 Polynomial predictive filter and fuzzy rules

Outliers

[53]

1

 Dempster–Shafer theory and mathematical modelling

Drift, noise

[75]

1