scholarly journals Covariance matrix forecasting using support vector regression

2021 ◽  
Author(s):  
Piotr Fiszeder ◽  
Witold Orzeszko
Keyword(s):  
Support Vector Regression ◽  
Covariance Matrix ◽  
Time Series Prediction ◽  
Dynamic Modelling ◽  
Positive Definiteness ◽  
Covariance Matrices ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Modelling And Forecasting

AbstractSupport vector regression is a promising method for time-series prediction, as it has good generalisability and an overall stable behaviour. Recent studies have shown that it can describe the dynamic characteristics of financial processes and make more accurate forecasts than other machine learning techniques. The first main contribution of this paper is to propose a methodology for dynamic modelling and forecasting covariance matrices based on support vector regression using the Cholesky decomposition. The procedure is applied to range-based covariance matrices of returns, which are estimated on the basis of low and high prices. Such prices are most often available with closing prices for many financial series and contain more information about volatility and relationships between returns. The methodology guarantees the positive definiteness of the forecasted covariance matrices and is flexible, as it can be applied to different dependence patterns. The second contribution of the paper is to show with an example of the exchange rates from the forex market that the covariance matrix forecasts calculated using the proposed approach are more accurate than the forecasts from the benchmark dynamic conditional correlation model. The advantage of the suggested procedure is higher during turbulent periods, i.e., when forecasting is the most difficult and accurate forecasts matter most.

scholarly journals The Impact of Data Segmentation in Predicting Monthly Building Energy Use with Support Vector Regression

2021 ◽  
pp. 69-76
Author(s):  
William Mounter ◽  
Huda Dawood ◽  
Nashwan Dawood
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Energy Use ◽  
Building Energy ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Energy Usage ◽  
Energy Prediction ◽  
Learning Techniques

AbstractAdvances in metering technologies and machine learning methods provide both opportunities and challenges for predicting building energy usage in the both the short and long term. However, there are minimal studies on comparing machine learning techniques in predicting building energy usage on their rolling horizon, compared with comparisons based upon a singular forecast range. With the majority of forecasts ranges being within the range of one week, due to the significant increases in error beyond short term building energy prediction. The aim of this paper is to investigate how the accuracy of building energy predictions can be improved for long term predictions, in part of a larger study into which machine learning techniques predict more accuracy within different forecast ranges. In this case study the ‘Clarendon building’ of Teesside University was selected for use in using it’s BMS data (Building Management System) to predict the building’s overall energy usage with Support Vector Regression. Examining how altering what data is used to train the models, impacts their overall accuracy. Such as by segmenting the model by building modes (Active and dormant), or by days of the week (Weekdays and weekends). Of which it was observed that modelling building weekday and weekend energy usage, lead to a reduction of 11% MAPE on average compared with unsegmented predictions.

scholarly journals PREDICTION OF GLUCOSE LEVEL IN DIABETICS WITH SUPPORT VECTOR REGRESSION

2020 ◽  
Vol 16 (1) ◽  
pp. 97-102
Author(s):  
Devi Wulandari ◽  
Agus Subekti
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Mean Squared Error ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Glucose Levels ◽  
Existing Problems ◽  
Learning Techniques ◽  
Comparison Algorithms ◽  
The Times

One of the common diabetes factors that people hear is that they consume too much or often consume sweet foods or drinks so that blood sugar in the human body increases. The times and increasingly sophisticated technology make it easier for someone to be able to predict a disease such as diabetes with machine learning techniques. Therefore, from the existing problems, a machine learning technique will be made in predicting glucose levels in diabetics. The aim is to predict glucose levels in diabetics and find the best algorithm from several comparison algorithms. The results of the experiments carried out by the support vector regression algorithm have a lower mean squared error value of 28.9480 compared to other comparative algorithms and visualize the error classification seen that Instance no 47 has a prediction of the highest plasma glucose value of 189.2305.

scholarly journals Prediction of Glucose Concentration in Blood Plasma with Support Vector Regression Algorithm

2019 ◽  
Vol 8 (6S) ◽  
pp. 352-355
Keyword(s):  
Support Vector Regression ◽  
Diabetes Management ◽  
Performance Measure ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Sets ◽  
Glucose Levels ◽  
Learning Techniques ◽  
And Performance ◽  
Near Future

Diabetes Mellitus is due to the disorder of glucose metabolism because of defects in insulin secretion or insulin action. It has become a major health challenge nowadays. Monitoring and regulation of blood glucose is inevitable to avoid diabetic complications. Prediction of near future glucose levels and giving alert for appropriate action could be done by machine learning techniques. This would greatly assist the diabetes patients in the daily management of diabetes. This paper discusses the effectiveness of Support Vector Regression in diabetes management. The methodology has been applied to three different data sets and performance measure is analyzed with Root Mean Square Error values.

Air Pollution Forecasting Model Based on Chance Theory and Intelligent Techniques

2017 ◽  
Vol 26 (06) ◽  
pp. 1750024 ◽  
Author(s):  
Nabil Mohamed Eldakhly ◽  
Magdy Aboul-Ela ◽  
Areeg Abdalla
Keyword(s):  
Support Vector Regression ◽  
Air Pollutant ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Forecasting Model ◽  
Chance Theory ◽  
Novel Approach ◽  
Learning Techniques ◽  
Greater Cairo ◽  
Hourly Concentration

A novel approach of weighted support vector regression (WSVR) technique with applied chance theory was proposed to build a robust forecasting model, called the chance weighted support vector regression (chWSVR) model. In order to forecast the particulate matter air pollutant of diameter less than 10 micrometers (PM10) one hour advance in the Greater Cairo Metropolitan Area (GCMA) in Egypt. The chance theory has advanced concepts pertinent to treat cases where both randomness and fuzziness play simultaneous roles at one time. The basic idea based on the proposed chWSVR model is assigning the chance weight value of the target variable, based on the chance theory, to its corresponding dataset point to become minimized in the objective function making that point more significant during the training process. Measuring data were collected and reprocessed from four monitoring stations located in GCMA and relative to the springs during the period from 2007 to 2010. The results of such model compared to similar ones built by other machine learning techniques, Random Forest and Bootstrap aggregating techniques. In all stations, comparing such models revealed that the proposed chWSVR model findings were promising in the forecasting of PM10 hourly concentration.

scholarly journals Computation of High-Performance Concrete Compressive Strength Using Standalone and Ensembled Machine Learning Techniques

Materials ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7034
Author(s):  
Yue Xu ◽  
Waqas Ahmad ◽  
Ayaz Ahmad ◽  
Krzysztof Adam Ostrowski ◽  
Marta Dudek ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Support Vector Regression ◽  
High Performance ◽  
Cross Validation ◽  
High Performance Concrete ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Fold Cross Validation

The current trend in modern research revolves around novel techniques that can predict the characteristics of materials without consuming time, effort, and experimental costs. The adaptation of machine learning techniques to compute the various properties of materials is gaining more attention. This study aims to use both standalone and ensemble machine learning techniques to forecast the 28-day compressive strength of high-performance concrete. One standalone technique (support vector regression (SVR)) and two ensemble techniques (AdaBoost and random forest) were applied for this purpose. To validate the performance of each technique, coefficient of determination (R2), statistical, and k-fold cross-validation checks were used. Additionally, the contribution of input parameters towards the prediction of results was determined by applying sensitivity analysis. It was proven that all the techniques employed showed improved performance in predicting the outcomes. The random forest model was the most accurate, with an R2 value of 0.93, compared to the support vector regression and AdaBoost models, with R2 values of 0.83 and 0.90, respectively. In addition, statistical and k-fold cross-validation checks validated the random forest model as the best performer based on lower error values. However, the prediction performance of the support vector regression and AdaBoost models was also within an acceptable range. This shows that novel machine learning techniques can be used to predict the mechanical properties of high-performance concrete.

scholarly journals Style Investing with Machine Learning

2016 ◽  
Vol 9 (12) ◽  
pp. 13
Author(s):  
Philipp Kallerhoff
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Efficiency Ratio ◽  
Economic Sectors ◽  
Style Investing ◽  
Regression Methods ◽  
Learning Techniques ◽  
Time Periods

<p>This paper applies machine learning techniques to style investing. Support Vector Regression is applied to multi-factor investing based on momentum, dividend, quality, volatility and growth. The results show that Support Vector Regression selects stocks consistently with a higher efficiency ratio than a broad market investment and outperforms linear regression methods. The methods are applied to global stocks in the MSCI World index between 1996 and 2016. The behavior of both models is analyzed for economic sectors and over time. Interestingly, factors like low-volatility and momentum contribute both positively and negatively in some economic sectors and certain time periods.</p>

scholarly journals Prediction of Rice Cultivation in India—Support Vector Regression Approach with Various Kernels for Non-Linear Patterns

2021 ◽  
Vol 3 (2) ◽  
pp. 182-198
Author(s):  
Kiran Kumar Paidipati ◽  
Christophe Chesneau ◽  
B. M. Nayana ◽  
Kolla Rohith Kumar ◽  
Kalpana Polisetty ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Rice Yield ◽  
Absolute Error ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
State Governments ◽  
Learning Techniques ◽  
Regression Approach ◽  
Rice Yields

The prediction of rice yields plays a major role in reducing food security problems in India and also suggests that government agencies manage the over or under situations of production. Advanced machine learning techniques are playing a vital role in the accurate prediction of rice yields in dealing with nonlinear complex situations instead of traditional statistical methods. In the present study, the researchers made an attempt to predict the rice yield through support vector regression (SVR) models with various kernels (linear, polynomial, and radial basis function) for India overall and the top five rice producing states by considering influence parameters, such as the area under cultivation and production, as independent variables for the years 1962–2018. The best-fitted models were chosen based on the cross-validation and hyperparameter optimization of various kernel parameters. The root-mean-square error (RMSE) and mean absolute error (MAE) were calculated for the training and testing datasets. The results revealed that SVR with various kernels fitted to India overall, as well as the major rice producing states, would explore the nonlinear patterns to understand the precise situations of yield prediction. This study will be helpful for farmers as well as the central and state governments for estimating rice yield in advance with optimal resources.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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