The nature of stock market movement has always been ambiguous for investors because of various influential factors. This study aims to significantly reduce the risk of trend prediction with machine learning and deep learning algorithms. Four stock market groups, namely diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange, are chosen for experimental evaluations. This study compares nine machine learning models (Decision Tree, Random Forest, Adaptive Boosting (Adaboost), eXtreme Gradient Boosting (XGBoost), Support Vector Classifier (SVC), Naïve Bayes, K-Nearest Neighbors (KNN), Logistic Regression and Artificial Neural Network (ANN)) and two powerful deep learning methods (Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators from ten years of historical data are our input values, and two ways are supposed for employing them. Firstly, calculating the indicators by stock trading values as continuous data, and secondly converting indicators to binary data before using. Each prediction model is evaluated by three metrics based on the input ways. The evaluation results indicate that for the continuous data, RNN and LSTM outperform other prediction models with a considerable difference. Also, results show that in the binary data evaluation, those deep learning methods are the best; however, the difference becomes less because of the noticeable improvement of models' performance in the second way.

The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were utilized for prediction of future values of stock market groups. We employed decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.

The intermittent and uncertain nature of wind places a premium on accurate wind power forecasting for the reliable and efficient operation of power grids with large-scale wind power penetration. Herein, six-month-ahead wind power forecasting models were developed using tree-based learning algorithms. Three models were developed to investigate the impact of input data on forecasting accuracy. The first model was trained with the average and standard deviation of wind speed values measured at a height of 40 m with a 10-min sampling time. To evaluate the impact of sampling time on model performance, a second model was trained with wind speed values measured at a height of 40 m with 1-h, 12-h, and 24-h sampling times. To assess the effect of measuring height on model accuracy, the third model was trained with wind speed values measured at 40 m extrapolated from values measured at heights of 30 m and 10 m. Experiments revealed that using longer time intervals and height extrapolation leads to considerable accuracy degradation in forecasted models. Finally, to study the generalization ability of the forecasted models, they were tested against wind data measured at heights and locations different from what the models had been trained with. Simulation results substantiated that tree-based learning algorithms can be successfully adopted not only for long-term wind power forecasting, but for potential wind power forecasting at different heights and geographical locations.

As power systems are gradually evolving into more efficient and intelligent cyber–physical energy systems with the large-scale penetration of renewable energies and information technology, they become increasingly reliant upon more accurate and complex forecasting. The accuracy and generalizability of the forecasting rest, to a great extent, upon the data quality, which is very susceptible to cyberattacks. False data injection (FDI) attacks constitute a class of cyberattacks that could maliciously alter a large portion of supposedly protected data, which may not be easily detected by existing operational practices, thereby deteriorating the forecasting performance causing catastrophic consequences in the power system. This article proposes a novel data-driven FDI attack detection mechanism to automatically detect the intrusions and thus enrich the reliability and resiliency of energy forecasting systems. The proposed mechanism is based on cross-validation, least-squares, and z -score metric providing accurate detections with low computational cost and high scalability without utilizing either system’s models or parameters. The effectiveness of the proposed detector is corroborated through six representative tree-based wind power forecasting models. Experiments indicate that corrupted data injected into input, output, and input–output data is properly located and removed, whereby the accuracy and generalizability of the final forecasts are recovered.

Recently, applications of three-dimensional (3-D) printers have extensively been increased in various industries. Fused deposition modeling process is one of the most widely used 3-D printing methods in this area due to its simplicity, reliability, and the ability to produce complex parts made of thermoplastic materials. In this research, composite sample parts consisted of copper particles with a constant 25 wt% of metallic powder as a filler and acrylonitrile butadiene styrene granules as a polymeric matrix. A filament production line to acquire printable filaments was applied and its optimum parameters were reported. Four printing parameters involved nozzle diameter, layer height, raster angle, and nozzle temperature were chosen in three levels for investigation of composite samples’ tensile strength, density, and production time as a new study. The Taguchi method, a well-known design of experiment tool, was employed to find the effect of each parameter and optimum levels with including the main effect, signal-to-noise ratio, and analysis of variance. Finally, optimum composite specimens manufactured by 3-D printer verified Taguchi method analysis and results.