Predictive analytics is concerned with the prediction of future trends and outcomes. The approaches used to conduct predictive analytics can be classified into machine learning techniques and regression techniques. Machine learning techniques have become increasingly popular in conducting predictive analytics due to their outstanding performance in handling large-scale datasets with uniform characteristics and noisy data. Innovative predictive models have been applied successfully in several domains such as health care, cyber security, education, credit card fraud detection, social media, cloud computing, software measurement, quality and defect prediction, cost and effort estimation and software reuse. The main focus of this special issue is on the current state of practice of machine learning techniques to address various predictive problems. A total of 14 papers are included in this special issue. A summary of the papers appears below. In ‘‘Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis’’ by Kiakojoori and Khorasani, the problem of health monitoring and prognosis of aircraft gas turbine engines using intelligent-based methodologies are investigated to support enhancement of flight safety and performance. Two different neural network-based architectures, namely the nonlinear autoregressive neural network with exogenous input (NARX) and the Elman neural network, are proposed and developed for predicting the health condition of the engine. Various degradations may occur in a given engine resulting in changes in the performance of its components. Two main degradation factors, namely the compressor fouling and the turbine erosion, are modeled and considered in this work. The proposed dynamic neural networks are first developed and applied to capture the degradation dynamics in the aircraft jet engine. The health condition of the engine is then predicted subject to occurrence of these deteriorations. Multiple scenarios are considered and extensive simulations are conducted corresponding to both dynamic neural network approaches. For each degradation scenario, several neural networks are trained and their performance in predicting the turbine exhaust temperature for multi-flights ahead is evaluated. The most suitable neural network for this prediction is then selected by using a normalized Bayesian information criterion model selection. Simulation results presented demonstrate and illustrate the effective performance of the proposed dynamic neural network-based prediction and prognosis strategies. In ‘‘A hybrid grid-GA-based LSSVR learning paradigm for crude oil price forecasting’’, Yu et al. propose a hybrid learning paradigm integrating least squares support vector regression (LSSVR) with a hybrid optimization searching approach for the parameters selection in the LSSVR in order to effectively model crude oil spot price with inherently high complexity. The grid method is first applied to determine the proper boundaries of the parameters in the LSSVR. Then, the genetic algorithm is implemented to & Ali Bou Nassif anassif@sharjah.ac.ae