Modelling and Forecasting Portfolio Inflows

This chapter analyses efficiency of support vector regression (SVR), artificial neural networks (ANNs), and structural vector autoregressive (SVAR) models in terms of in-sample forecasting of portfolio inflows (PIs). Time series daily data sourced from Rand Merchant Bank (RMB) covering the period of 1st March 2004 to 1st February 2016 were used. Mean squared error, root mean squared error, mean absolute error, mean absolute squared error, and root mean scaled log error were used to evaluate model performance. The results showed that SVR has the best modelling performance when compared to others. In determining factors that affect allocation of PIs into South Africa based on SVAR, 69% of the variation was explained by pull factors while 9% was explained by push factor. Hence, SVR model is more accurate than ANNs. This chapter therefore recommends that banking sector particularly RMB should use machine learning technique in modelling PIs for a better financial solution.

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Modelling and Forecasting Portfolio Inflows

Handbook of Research on Smart Technology Models for Business and Industry - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-3645-2.ch014 ◽

2020 ◽

pp. 329-350

Author(s):

Mogari I. Rapoo ◽

Elias Munapo ◽

Martin M. Chanza ◽

Olusegun Sunday Ewemooje

Keyword(s):

Banking Sector ◽

Mean Squared Error ◽

Model Performance ◽

Absolute Error ◽

Support Vector ◽

Vector Autoregressive ◽

Squared Error ◽

Daily Data ◽

Determining Factors ◽

Push Factor

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Visualization & Prediction of COVID-19 Future Outbreak by Using Machine Learning

International Journal of Information Technology and Computer Science ◽

10.5815/ijitcs.2021.03.02 ◽

2021 ◽

Vol 13 (3) ◽

pp. 16-32

Author(s):

Ahmed Hassan Mohammed Hassan ◽

◽

Arfan Ali Mohammed Qasem ◽

Walaa Faisal Mohammed Abdalla ◽

Omer H. Elhassan

Keyword(s):

Machine Learning ◽

Polynomial Regression ◽

Mean Squared Error ◽

Absolute Error ◽

Future Perspective ◽

Support Vector ◽

Squared Error ◽

Vector Machines ◽

The World ◽

Negative Factors

Day by day, the accumulative incidence of COVID-19 is rapidly increasing. After the spread of the Corona epidemic and the death of more than a million people around the world countries, scientists and researchers have tended to conduct research and take advantage of modern technologies to learn machine to help the world to get rid of the Coronavirus (COVID-19) epidemic. To track and predict the disease Machine Learning (ML) can be deployed very effectively. ML techniques have been anticipated in areas that need to identify dangerous negative factors and define their priorities. The significance of a proposed system is to find the predict the number of people infected with COVID19 using ML. Four standard models anticipate COVID-19 prediction, which are Neural Network (NN), Support Vector Machines (SVM), Bayesian Network (BN) and Polynomial Regression (PR). The data utilized to test these models content of number of deaths, newly infected cases, and recoveries in the next 20 days. Five measures parameters were used to evaluate the performance of each model, namely root mean squared error (RMSE), mean squared error (MAE), mean absolute error (MSE), Explained Variance score and r2 score (R2). The significance and value of proposed system auspicious mechanism to anticipate these models for the current cenario of the COVID-19 epidemic. The results showed NN outperformed the other models, while in the available dataset the SVM performs poorly in all the prediction. Reference to our results showed that injuries will increase slightly in the coming days. Also, we find that the results give rise to hope due to the low death rate. For future perspective, case explanation and data amalgamation must be kept up persistently.

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Support Vector Machine to Predict Electricity Consumption in the Energy Management Laboratory

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v5i3.2947 ◽

2021 ◽

Vol 5 (3) ◽

pp. 466-473

Author(s):

Azam Zamhuri Fuadi ◽

Irsyad Nashirul Haq ◽

Edi Leksono

Keyword(s):

Support Vector Machine ◽

Energy Management ◽

Mean Squared Error ◽

Measurement Data ◽

Electricity Consumption ◽

Absolute Error ◽

Support Vector ◽

Mean Error ◽

Squared Error ◽

Electrical Loads

Predicted electricity consumption is needed to perform energy management. Electricity consumption prediction is also very important in the development of intelligent power grids and advanced electrification network information. we implement a Support Vector Machine (SVM) to predict electrical loads and results compared to measurable electrical loads. Laboratory electrical loads have their own characteristics when compared to residential, commercial, or industrial, we use electrical load data in energy management laboratories to be used to be predicted. C and Gamma as searchable parameters use GridSearchCV to get optimal SVM input parameters. Our prediction data is compared to measurement data and is searched for accuracy based on RMSE (Root Square Mean Error), MAE (Mean Absolute Error) and MSE (Mean Squared Error) values. Based on this we get the optimal parameter values C 1e6 and Gamma 2.97e-07, with the result RSME (Root Square Mean Error) ; 0.37, MAE (meaning absolute error); 0.21 and MSE (Mean Squared Error); 0.14.

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Predicting and Analysing the Behaviour of COVID-19

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit217213 ◽

2021 ◽

pp. 40-46

Author(s):

Gaurav Singh ◽

Shivam Rai ◽

Himanshu Mishra ◽

Manoj Kumar

Keyword(s):

Machine Learning ◽

Polynomial Regression ◽

Mean Squared Error ◽

Absolute Error ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Systems Science ◽

Data Repository ◽

Support Vector ◽

Squared Error

The prime objective of this work is to predicting and analysing the Covid-19 pandemic around the world using Machine Learning algorithms like Polynomial Regression, Support Vector Machine and Ridge Regression. And furthermore, assess and compare the performance of the varied regression algorithms as far as parameters like R squared, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. In this work, we have used the dataset available on Covid-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at John Hopkins University. We have analyzed the covid19 cases from 22/1/2020 till now. We applied a supervised machine learning prediction model to forecast the possible confirmed cases for the next ten days.

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Equations Relating Compacted and Uncompacted Live Crown Ratio for Common Tree Species in the South

Southern Journal of Applied Forestry ◽

10.1093/sjaf/34.3.118 ◽

2010 ◽

Vol 34 (3) ◽

pp. 118-123 ◽

Cited By ~ 6

Author(s):

KaDonna C. Randolph

Keyword(s):

Mean Squared Error ◽

Model Performance ◽

Absolute Error ◽

Logistic Function ◽

Root Mean Squared Error ◽

Fit Statistics ◽

Squared Error ◽

Hardwood Species ◽

Using Data ◽

Species Specific

Abstract Species-specific equations to predict uncompacted crown ratio (UNCR) from compacted live crown ratio (CCR), tree length, and stem diameter were developed for 24 species and 12 genera in the southern United States. Using data from the US Forest Service Forest Inventory and Analysis program, nonlinear regression was used to model UNCR with a logistic function. Model performance was evaluated with standard fit statistics (root mean squared error, mean absolute error, mean error, and model efficiency) and by comparing the results of using the observed and predicted UNCR values in secondary applications. Root mean squared error for the regression models ranged from 0.062 to 0.176 UNCR and averaged 0.114 UNCR across all models. Height to live crown base calculations and crown width estimations based on the observed and predicted UNCR values were in close agreement. Overall, the models performed well for the Pinus and Taxodium genera and several individual hardwood species; however, model performance was generally poor for the Acer, Quercus, and Carya genera.

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Forecasting of Air Pollution Index PM2.5 Using Support Vector Machine(SVM)

Journal of Computing Research and Innovation ◽

10.24191/jcrinn.v5i3.149 ◽

2020 ◽

Vol 5 (3) ◽

pp. 43-53

Author(s):

Nor Hayati Binti Shafii ◽

Rohana Alias ◽

Nur Fithrinnissaa Zamani ◽

Nur Fatihah Fauzi

Keyword(s):

Air Pollution ◽

Support Vector Machine ◽

Air Quality ◽

Mean Squared Error ◽

Mean Absolute Error ◽

Pollution Index ◽

Absolute Error ◽

Support Vector ◽

Air Pollution Index ◽

Squared Error

Air pollution is a current monitored problem in areas with high population density such as big cities. Many regions in Malaysia are facing extreme air quality issues. This situation is caused by several factors such as human behavior, environmental awareness and technological development. Accessing the air pollution index (API) accurately is very important to control its impact on environmental and human health. The work presented here aims to access air pollution index of PM2.5 using Support Vector Machine (SVM) and to compare the accuracy of four different types of the kernel function in Support Vector Machine (SVM). The data used is provided by the Department of Environment (DOE) and it is recorded from two Continuous Air Quality Monitoring Stations (CAQM) located at Tanah Merah and Kota Bharu. The results are analyzed using mean absolute error (MAE) and root mean squared error (RMSE). It is found that the proposed model using Radial Basis Function (RBF) with its parameters of cost and gamma equal to 100 can effectively and accurately forecast the air pollution index with Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 0.03868583 and 0.06251793 respectively for API in Kota Bharu and 0.03857308 (MAE) and 0.05895648 (RMSE) for API in Tanah Merah.

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Soft Computing Techniques for Weather Change Predictions in Delhi

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d7382.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 793-800

Keyword(s):

Soft Computing ◽

Mean Squared Error ◽

Weather Forecasting ◽

Weather Prediction ◽

Research Work ◽

Absolute Error ◽

Support Vector ◽

Squared Error ◽

Proposed Model ◽

Soft Computing Techniques

Weather forecasting and warning is the application of science and technology to predict the state of the weather for a future time of a given location. The emergence of adverse effects of weather has endangered the life of general public in previous years. The unpredicted flood and super cyclone in many places have created havoc. The government and private agencies are working on its behaviours but still it is challenging and incomplete. But, the application of soft computing techniques in weather prediction has made a significant perfomance now a days. This research work presents the comparative study of soft computing techniques like MultiLayer Perceptron(MLP), Support Vector Machine(SVM) and J48 Decision Tree for forecasting the weather of Delhi with ten years data comprising of temperature, dew, humidity, air pressure, wind speed and visibility. This paper tries to describe the comparison among above models using four different error values like Relative Absolute Error(RAE), Mean Absolute Error(MAE), Root Mean Squared Error(RMSE) and Root Relative Squared Error(R2 ) with a proposed model by defining new algorithm. Further the performance can be enhanced if textmining will be applied in this proposed model.

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Comparison between SARIMA and Holt–Winters models for forecasting monthly streamflow in the western region of Cuba

SN Applied Sciences ◽

10.1007/s42452-021-04667-5 ◽

2021 ◽

Vol 3 (6) ◽

Author(s):

Gustavo Reinel Alonso Brito ◽

Anaily Rivero Villaverde ◽

Andrés Lau Quan ◽

María Elena Ruíz Pérez

Keyword(s):

Cross Validation ◽

Mean Squared Error ◽

Model Performance ◽

Absolute Error ◽

List Type ◽

Model Assessment ◽

Squared Error ◽

Monthly Streamflow ◽

One Year ◽

Sarima Models

Abstract The present study aims to compare SARIMA and Holt–Winters model forecasts of mean monthly flow at the V Aniversario basin, western Cuba. Model selection and model assessment are carried out with a rolling cross-validation scheme using mean monthly flow observations from the period 1971–1990. Model performance is analyzed in one- and two-year forecast lead times, and comparisons are made based on mean squared error, root mean squared error, mean absolute error and the Nash–Sutcliffe efficiency; all these statistics are computed from observed and simulated time series at the outlet of the basin. The major findings show that Holt–Winters models had better performance in reproducing the mean series seasonality when the training observations were insufficient, while for longer training subsets, both models were equally competitive in forecasting one year ahead. SARIMA models were found to be more reliable for longer lead-time forecasts, and their limitations after being trained on short observation periods are due to overfitting problems. Article Highlights Comparison based on rolling cross-validation revealed the models forecasts sensibility to available observations amount. HW and SARIMA models perform better when limited observations or long-view forecasting, respectively, otherwise they do similar. HW models were superior modeling less variable monthly flows while SARIMA models better forecast the highly variable periods.

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Automated Outlier Detection for Electrical Motors and Transformers

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9304 ◽

2020 ◽

Vol 17 (9) ◽

pp. 4703-4708

Author(s):

K. Anitha Kumari ◽

Avinash Sharma ◽

S. Nivethitha ◽

V. Dharini ◽

V. Sanjith ◽

...

Keyword(s):

Machine Learning ◽

Polynomial Regression ◽

Mean Squared Error ◽

Absolute Error ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Squared Error ◽

Electrical Motors

Electrical appliances most commonly consist of two electrical devices, namely, electrical motors and transformers. Typically, electrical motors are normally used in all sort of industrial purposes. Failures of such motors results in serious problems, such as overheat, shut down and even burnt, in their host systems. Thus, more attention have to be paid in detecting the outliers. In a similar way, to avoid the unexpected power reliability problems and system damages, the prediction of the failures in the transformers is expected to quantify the impacts. By predicting the failures, the lifetime of the transformers increases and unnecessary accidents is avoided. Therefore, this paper presents the detection of the outliers in electrical motors and failures in transformers using supervised machine learning algorithms. Machine learning techniques such as Support Vector Machine (SVM), Random Forest (RF) and regression techniques like Support Vector Regression (SVR), Polynomial Regression (PR) are used to analyze the use cases of different motor specifications. Evaluation and the efficiency of findings are proved by considering accuracy, precision, F-measure, and recall for motors. Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE) and R-squared Error (R2) are considered as metrics for transformers. The proposed approach helps to identify the anomalies like vibration loss, copper loss and overheating in the industrial motor and to determine the abnormal functioning of the transformer that in turn leads to ascertain the lifetime. The proposed system analyses the behaviour of the electrical machines using the energy meter data and reports the outliers to users. It also analyses the abnormalities occurring in the transformer using the parameters involved in the degradation of the paper-oil insulation system and the voltage of operation as a whole leads to the predict the lifetime.

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Prediction Of Use Of Electric Kwh Using Support Vector Regression (SVR) Method (Case Study: Pt Pln (Persero) Rayon Seririt)

Repositor ◽

10.22219/repositor.v2i4.106 ◽

2020 ◽

Vol 2 (4) ◽

pp. 525

Author(s):

Rima Mediana ◽

Setio Basuki ◽

Nur Hayatin

Keyword(s):

Radial Basis Function ◽

Support Vector Regression ◽

Basis Function ◽

Mean Squared Error ◽

Mean Absolute Error ◽

Absolute Error ◽

Support Vector ◽

Root Mean Squared Error ◽

Squared Error ◽

Radial Basis

AbstrakPeranan listrik sangat penting bagi kehidupan masyarakat, begitu pentingnya peranan listrik tentu saja berdampak pada kebutuhan listrik yang begitu besar, maka PT. PLN (Persero) Rayon Seririt sebagai penyedia tenaga listrik harus bisa memprediksi besarnya peggunaan listrik rumah tangga setiap harinya. Selain itu menyebabkan semakin besar pula pemakian kwh listik, apabila pemakaian kwh listrik tidak diolah dengan baik akan menimbulkan beban energi listrik yang tidak terbendung. Dengan permasalahan yang telah diuraikan, penelitian ini menerapkan algoritma Support Vector Regression dalam Prediksi Pemakain KWH Listrik untuk mengetahui besarnya pemakaian kwh listrik yang akan datang. Berdasarkan hasil pengujian yang dilakukan hasil nilai akurasi terbaik Mean Absolute Error (MAE) sebesar 133560,1, Root Mean Squared Error (RMSE) sebesar 167664,1, dan Koefisien Korelasi sebesar 84,0 pada kernel polynomial. Sehingga algoritma Support Vector Regression dan fungsi kernel Radial Basis Function (RBF) cocok digunakan dalam memprediksi pemakaian kwh listrik.AbstractThe role of electricity is really significant for societies' live and it brings the huge impacts on the needs of electricity. This circumstance makes PT. PLN (Persero) Rayon Seririt as the provider of electricity must be able to predict the amount of household electricity usage steadily. This also causes the greater use of kwh electricity, if the use of kwh electricity is not treated properly, it will cause the burden of electrical energy is unstoppable. Through the problems that have been elaborated, this study implements the Support Vector Regression algorithm in the prediction of kwh electricity usage to know the amount of kwh electricity usage that will come.Based on the results of tests that have been conducted, the result of best accuracy value Mean Absolute Error (MAE) equal to 133560,1, Root Mean Squared Error (RMSE) equal to 133560,1, and Correlation Coefficient equal to 84,0 at Radial Base Function kernel. It means, the Support Vector Regression algorithm and Radial Basis Function kernel function (RBF) are suitable to predict the use of kwh electricity.

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