# Data Mining Algorithms for Smart Cities: A Bibliometric Analysis
## 2 Conceptual Framework
### Forecasting  
Forecasting algorithms facilitate the process of making statements about events whose actual outcomes (typically) have not yet been observed [58]. For instance, [PERSON] et al. [35] implemented a time series forecasting analysis with the adoption of three different techniques, such as exponential smoothing state-space model (ETS), ARIMA, and Theta. They also tried a control method (Mean), but they received worse results. [PERSON] et al. [92] used the Auto-Regressive Integrated Moving Average (ARIMA) model as a forecasting method. ARIMA model is composed of two parts: Auto-Regressive and Moving Average. The Auto-Regressive part (AR) creates the basis of the prediction and can be improved by a Moving Average (MA) modeling for errors made in previous time instants of prediction. The order of ARIMA models is defined by the parameters (p; d; q): p is the order of AR model; d is the degree of differencing, and q is the order of the MA part, respectively; and by the corresponding seasonal counterparts (P; D; Q). ARIMA can be used for planning, as well, as it poses the basis to be used as an instrument for early warning: that is, for detecting dysfunctions as unexpected patterns in the city users' behavior [8]. The Markov forecasting model can be applied to make governance decisions according to results generated by the ontology model, and a sequential evaluation model can also be built to assess the effect of governance decisions, which effectually optimizes the Markov forecasting model [44]. As there is a need for algorithms that have low-enough computational complexity to run on the devices found in the smart city infrastructure, [PERSON] et al. [68] leverage Taylor Expanded Analog Forecasting Algorithm (TESLA), implementation a statistical learning model that can be fully generalized, as the data translation algorithm in their context engine. [PERSON] et al. employed the Granger Causality Test (GCT) in their study [42]. GCT is a statistical hypothesis test for determining whether one time series has causal relationships with another. One model that deals with numeric variables is Linear Regression (LinR), where a single linear formula represents the mapping from input to class values. Regression analysis aims to model the relationship between a dependent variable and one or more explanatory independent variables. Among linear regression models, [PERSON] selected Least Squares (LS) in the work of [76], which fits a linear model to the data by minimizing the total squared error. Logistic regression (LogR) is a linear, fairly simple classifier widely used in many classification applications. The basic idea is that for each instance, we model the posterior probability of the actionable class as a logistic function with parameters that weigh the features f and an offset \(\beta\)[52]. LogR is popular in the medical literature because it predicts a probability of a sample belonging to the positive class [93]. If data have a large number of features, we can also use a nonlinear regression technique, which complements the linear regression method, the Support Vector Regression (SVR) [92]. Dynamic Time Warping (DTW) is an algorithm for comparing two given (time-dependent) sequencesthat may vary in speed, applied to time series analysis video, audio, and graphics data sequences [114]. Anomaly detection, also known as outlier detection or novelty detection, is an important research domain of data analysis. It aims to detect a few data patterns that do not conform to the expected data characteristics [115]. An anomaly can be defined as unusual or abnormal behavior [116]. To excavate the latent information of data and construct the compact data representation, [PERSON] et al. proposed a generative Dictionary Learning Model (DLM) for anomaly detection [117], which jointly learns the latent representations and their recombination basis. Dictionary learning aims to learn a set of basis vectors that can encode all feature data into compact form with a linear combination.