## 1 Introduction
### Predictive Analytics and Data Mining in Production Time Estimation  
PAs are powerful analytic techniques for dealing with data. The goal of the application of PAs is to learn from the data, to achieve the prediction of a certain phenomenon [7] by applying algorithms in ML. For production time estimation in research and industry PAs of Supervised Learning (SL), Unsupervised Learning (UL), Ensembled Learning (EL), and Deep Learning (DL) are applied. These are summarized in Figure 1. PAs are used in the sense of building a model for estimation that depends on attributes. Attributes are mostly described by time or specific data. SL is divided into classification methods and regression. Classification models select the estimated value from training values [26; 38; 40] by defining constraints on the selection of the assumed nominal value. The predicted nominal values can be in the form of an interval or a single value. The inputs and outputs of classification algorithms are processed as discrete values. Neural networks (NNs) are often used for classification, almost for modeling of cycle time (CT) and lead time (LT) estimation [38; 41; 42; 43]. For the flow shop, feedforward NN  
Figure 1: PA learning methods applied for estimating production time. with attributes number of operations, product type, and queuing times [44] is applied. In addition, a recurrent NN with the attributes of machine operating status, processing time, target make span, machine parameters, and mean time to failure and repair [37] is presented. The modeling of NN is time-consuming, therefore NN models are used for time estimation of products with high repeatability. Decision trees are applied to estimate CT [45] and LT [46]. These methods categorize estimated values into disjoint solution sets by constraining attribute values in accordance with the induction strategy [47; 48]. The k-nearest neighbor method belongs to the subset of methods known as lazy learning [49]. It does not consider all values but rather recognizes values that have already appeared in the data on the basis of which other values are then classified. K-nearest neighbor is applied for CT estimation of wall panels in a flow shop production system [26]. The Support Vector Machine (SVM) method defines a linear decision boundary. SVM uses only a small subset of values that lie on the boundaries of the classes, and it identifies critical points that lie in different classes, called support vectors [49]. The method then defines their connecting line, where the boundary between classes is a perpendicular bisector passing through the midpoint of this line, known as the maximum margin hyperplane. SVM is presented for time estimation of manual activities [50; 51]. Naive Bayes (Bayesian probability) is used when the attributes are independent and equally important. It is based on conditional probability. The algorithm selects the most frequent value and the most frequent values of the attributes associated with it [49]. Naive Bayes within job shop with CNC, turning, and milling for LT estimation is presented [52]. Regression models compute the time estimation value. The regression is a continuous function with coefficients corresponding to the weights of each attribute in the time estimation. The simplest form of regression is the simple linear regression model. It is an estimation based on one attribute. In multiple linear regression, the target value depends on multiple input attributes, and the problem is solved in vector space. CT models for machining [53] and assembly [22; 54] are presented. The fuzzy regression model is based on uncertain data. This method finds application in the electronics industry, for the CT estimation of the production of wafers [55]. UL is the application of a set of tools used for data processing [56]. The purpose of UL is to find relationships in the data, discover subsets, or reduce the data [57], to prepare the data for the application of SL. Clustering algorithms are rules for the discovery of similar subsets of data [41; 58].