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 continues 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 continues 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.
Fused deposition modeling (FDM) is the trendiest threedimensional (3D) printing method among additive manufacturing technologies. In this process, the final parts are constructed through layer-by-layer adhesion of thermoplastic polymers. Amorphous thermoplastic polymers have better printability compared to semicrystalline ones; so, they are most popular with FDM users. Generally, the overall mechanical properties of FDM 3D printed parts are weaker in comparison to the traditional methods (such as injection molding) due to the weak bonds between the deposited rasters and layers. Therefore, the introduction of new materials with higher mechanical properties and easy printing process of the semicrystalline polymers has always been challenging to progress the mechanical properties of the products. In this study by the FDM process, the effect of nozzle temperature and heat treatment (annealing) on the mechanical properties of high-temperature polylactic acids is investigated. The increase in the nozzle temperature develops the rasters and layers bonding, and the heat treatment of the parts after printing rises the crystallinity percentage, which is crucial for the improvement of mechanical properties. Experimental results show that an increase in the nozzle temperature raises the tensile strength and modulus to 65.7 MPa and 4.97 GPa, respectively. Furthermore, the heat treatment process increases the tensile strength and modulus up to 67.4 MPa and 5.65 GPa. The final tensile modulus values are the highest ones reported for pure materials printed by the FDM process. POLYM. ENG. SCI., 60:979-987, 2020.
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.
Prediction of stock groups values has always been attractive and challenging for shareholders. 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 are chosen for experimental evaluations. Data are collected for the groups based on ten years of historical records. The values predictions are created for 1, 2, 5, 10, 15, 20 and 30 days in advance. The machine learning algorithms 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 network (ANN), Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators are selected as the inputs into each of the prediction models. Finally, the result of predictions is presented for each technique based on three metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. Also, 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.INDEX TERMS Wind energy, long-term, wind power forecasting, machine learning, regression.
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