scholarly journals Penalized Logistic Regressions with Technical Indicators Predict Up and Down Trends

2021 ◽  
Author(s):  
Huifeng Jiang ◽  
Xuemei Hu ◽  
Hong Jia
Keyword(s):  
Logistic Regression ◽  
Stock Prices ◽  
Prediction Method ◽  
Roc Curves ◽  
Least Square ◽  
Support Vector ◽  
Absolute Deviation ◽  
Technical Indicators ◽  
Coordinate Descent Algorithm ◽  
Logistic Regressions

Abstract Predicting up and down trends for stock prices is an important puzzle in the financial field. Hu & Jiang (2021) proposed logistic regression with 6 technical indicators to predict up and down trends for Google's stock prices. In this paper we further propose the five penalized logistic regressions with 19 technical indicators: ridge (L2), lasso (L1), elastic net(EN), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP) to improve the prediction accuracy. Firstly, we combine the iterative weighted least square algorithm with the coordinate descent algorithm, and apply a training set to obtain parameter estimators and probability estimators. Then we adopt a test set to construct confusion matrices and receiver operating characteristic (ROC) curves, and apply them to assess their prediction performances. Finally we compare the proposed five prediction methods with logistic regression, support vector machine (SVM) and artificial neural network (ANN) , and found that the MCP penalized logistic regression performs the best. Therefore, we develop a new efficient prediction method to predict up and down trends for stock prices.

Research on Combined Prediction Model for Busy Telephone Traffic

2014 ◽  
Vol 610 ◽  
pp. 789-796
Author(s):  
Jiang Bao Li ◽  
Zhen Hong Jia ◽  
Xi Zhong Qin ◽  
Lei Sheng ◽  
Li Chen
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Low Frequency ◽  
Prediction Method ◽  
Pso Algorithm ◽  
Frequency Component ◽  
Least Square ◽  
High Frequency Component ◽  
Support Vector ◽  
Telephone Traffic

In order to improve the prediction accuracy of busy telephone traffic, this study proposes a busy telephone traffic prediction method that combines wavelet transformation and least square support vector machine (lssvm) model which is optimized by particle swarm optimization (pso) algorithm. Firstly, decompose the pretreatment of busy telephone traffic data with mallat algorithm and get low frequency component and high frequency component. Secondly, reconfigure each component and use pso_lssvm model predict each reconfigured one. Then the busy telephone traffic can be achieved. The experimental results show that the prediction model has higher prediction accuracy and stability.

scholarly journals The use of entropy based fuzzy membership on weighted logistic regression for the unbalanced data

2021 ◽  
Vol 880 (1) ◽  
pp. 012048
Author(s):  
Ajiwasesa Harumeka ◽  
Santi Wulan Purnami ◽  
Santi Puteri Rahayu
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Rare Event ◽  
Least Square ◽  
Fuzzy Membership ◽  
Support Vector ◽  
Unbalanced Data ◽  
Fuzzy Support Vector Machine ◽  
Minority Class ◽  
Weighted Logistic Regression

Abstract Logistic regression is a popular and powerful classification method. The addition of ridge regularization and optimization using a combination of linear conjugate gradients and IRLS, called Truncated Regularized Iteratively Re-weighted Least Square (TR-IRLS), can outperform Support Vector Machine (SVM) in terms of processing speed, especially when applied to large data and have competitive accuracy. However, neither SVM nor TR-IRLS is good enough when applied to unbalanced data. Fuzzy Support Vector Machine (FSVM) is an SVM development for unbalanced data that adds fuzzy membership to each observation. The fuzzy membership makes the interest of each observation in the minority class higher than the majority class. Meanwhile, TR-IRLS developed into a Rare Event Weighted Logistic Regression (RE-WLR) by adding weight to logistic regression and bias correction. The weighting of the RE-WLR depends on the undersampling scheme. It allows an “information loss”. Between FSVM and RE-WLR has a similarity, the weight based only on class differences (minority or majority). Entropy Based Fuzzy Support Vector Machine (EFSVM) is a method used to accommodate the weaknesses of FSVM by considering the class certainty of class observations. As a result, EFSVM is able to improve SVM performance for unbalanced data, even beating FSVM. For this reason, we use EF on the TR-IRLS algorithm to classify large and unbalanced data, as a proposed method. This method is called Entropy-Based Fuzzy Weighted Logistic Regression (EF-WLR). This Research shows the review of EF-WLR for unbalanced data classification.

Research on Prediction Method of Soft Soil Foundation Settlement

2011 ◽  
Vol 97-98 ◽  
pp. 36-39
Author(s):  
Xiao Ma Dong
Keyword(s):  
Support Vector Machine ◽  
Soft Soil ◽  
Prediction Method ◽  
Least Square ◽  
Support Vector ◽  
Prediction Methods ◽  
Foundation Settlement ◽  
Soil Foundation ◽  
Soft Soil Foundation ◽  
Curve Fitting Method

The current prediction methods of foundation settlement have biggish error under the condition of lesser foundation settlement observational datum. Aim at the localization of present prediction methods and the virtues of Support Vector Machine arithmetic, the method of predicting soft soil foundation settlement based on Least Square Support Vector Machine (LS-SVM) was proposed in this paper and compared with the neural network method and curve fitting method. The research results show that this proposed method is feasible and effective for predicting soft soil foundation settlement. Least Square Support Vector Machine provides a more advanced method than these conventional methods for predicting foundation settlement.

A prediction method for deck-motion based on online least square support vector machine and genetic algorithm

2018 ◽  
Vol 24 (2) ◽  
pp. 382-397
Author(s):  
Xixiang Liu ◽  
Qiming Wang ◽  
Rong Huang ◽  
Songbing Wang ◽  
Xianjun Liu
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Prediction Method ◽  
Least Square ◽  
Support Vector

Application of Grey Least Square Support Vector Machine in New Equipment Materiel Demand Prediction

2014 ◽  
Vol 602-605 ◽  
pp. 3333-3337
Author(s):  
Shuang Shuang Yu ◽  
Tie Ning Wang ◽  
Ning Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Method ◽  
Original Data ◽  
Small Sample ◽  
Least Square ◽  
Support Vector ◽  
Grey Prediction ◽  
Demand Prediction ◽  
Efficient Prediction ◽  
General Demand

Due to the short investment time of the new equipment, the materiel consumption and maintenance data is not much. As a result, its demand prediction belongs to the prediction of small sample data. Since general demand prediction methods are difficult to predict the materiel demand of new equipment, an applicable and efficient prediction method should be explored to solve the problem. Therefore, combining grey prediction theory and least square support vector machine and operating accumulative generation on the original data sequence to extract its deep law characteristic, the new equipment materiel demand prediction model based on Grey Least Square Support Vector Machine (GLSSVM) was established, and the model's parameters was optimized by SIWPSO. Finally an example was set using Neural Network, traditional LSVSM and GLSSVM to predict the materiel demand of new equipment X to verify the accuracy and effectiveness of GLSSVM. The result shows that the prediction precision of GLSSVM is superior to the other two methods.

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