Abstract Analysis of time series data and accurate prediction of future values are among the most challenging tasks that the data analysts face in many fields. Forecasting of energy demands is very essential because both insufficient and excess energy production may lead to a significant reduction of benefits and high storage costs respectively. In order to discover the regularities in dynamic and non stationary... Analysis of time series data and accurate prediction of future values are among the most challenging tasks that the data analysts face in many fields. Forecasting of energy demands is very essential because both insufficient and excess energy production may lead to a significant reduction of benefits and high storage costs respectively. In order to discover the regularities in dynamic and non stationary data, improved time series forecasting requires a model that combines multiple prediction models. The Ensemble approach performs better than single learning model and discovers the dynamic patterns in Energy time series data. In this paper, we compare the performance of two different Ensemble learning techniques; Bagging (Bootstrap Aggregating) and stacking in forecasting energy time series data. Stacking technique used in this paper, combines different classifiers like Radial Basis Function (RBF), Multilayer perceptron (MLP) and Support Vector Machine (SVM) Read More Read Less

Abstract This paper applies structure equation model, which is capable of processing variables and their relationship, in ecological security analysis of coal mining areas. Based on the collection of data from questionnaires and in-depth interviews, and then by using SPSS and AMOS software, it analyzes the factors of the ecological security in coal mining areas, sets up the paths in the model, and establishes... This paper applies structure equation model, which is capable of processing variables and their relationship, in ecological security analysis of coal mining areas. Based on the collection of data from questionnaires and in-depth interviews, and then by using SPSS and AMOS software, it analyzes the factors of the ecological security in coal mining areas, sets up the paths in the model, and establishes an evaluative index system for ecological security in coal mining areas. The research results prove that this model has a good emulation effect, and that it can reflect the relationship between ecological security factors. The model has good interpretive potential, and can be used in practice. Read More Read Less