Market Trends Study Based on Moving Average Slope and Support Vector Machine

2010 ◽  
Author(s):  
Jian Qingming ◽  
Zeng Huanglin
Keyword(s):  
Support Vector Machine ◽  
Moving Average ◽  
Support Vector ◽  
Average Slope ◽  
Market Trends

scholarly journals Forecasting hydrologic parameters using linear and nonlinear stochastic models

2019 ◽  
Vol 11 (4) ◽  
pp. 1284-1301
Author(s):  
Hamed Nozari ◽  
Fateme Tavakoli
Keyword(s):  
Support Vector Machine ◽  
Moving Average ◽  
Sustainable Use ◽  
Rain Gauge ◽  
Support Vector ◽  
Autoregressive Moving Average ◽  
Monthly Precipitation ◽  
Precipitation Prediction ◽  
Armax Model ◽  
Monthly Discharge

Abstract One of the most important bases in the management of catchments and sustainable use of water resources is the prediction of hydrological parameters. In this study, support vector machine (SVM), support vector machine combined with wavelet transform (W-SVM), autoregressive moving average with exogenous variable (ARMAX) model, and autoregressive integrated moving average (ARIMA) models were used to predict monthly values of precipitation, discharge, and evaporation. For this purpose, the monthly time series of rain-gauge, hydrometric, and evaporation-gauge stations located in the catchment area of Hamedan during a 25-year period (1991–2015) were used. Out of this statistical period, 17 years (1991–2007), 4 years (2008–2011), and 4 years (2012–2015) were used for training, calibration, and validation of the models, respectively. The results showed that the ARIMA, SVM, ARMAX, and W-SVM ranked from first to fourth in the monthly precipitation prediction and SVM, ARIMA, ARMAX, and W-SVM were ranked from first to fourth in the monthly discharge and monthly evaporation prediction. It can be said that the SVM has fewer adjustable parameters than other models. Thus, the model is able to predict hydrological changes with greater ease and in less time, because of which it is preferred to other methods.

EEG Signals Classification Using Support Vector Machine

2020 ◽  
Vol 12 (2) ◽  
pp. 215-224
Author(s):  
Abdelhakim Ridouh ◽  
Daoud Boutana ◽  
Salah Bourennane
Keyword(s):  
Support Vector Machine ◽  
Wavelet Decomposition ◽  
Moving Average ◽  
Real Life ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Decomposition Level ◽  
Sensitivity Specificity

We address with this paper some real-life healthy and epileptic EEG signals classification. Our proposed method is based on the use of the discrete wavelet transform (DWT) and Support Vector Machine (SVM). For each EEG signal, five wavelet decomposition level is applied which allow obtaining five spectral sub-bands correspond to five rhythms (Delta, Theta, Alpha, Beta and gamma). After the extraction of some features on each sub-band (energy, standard deviation, and entropy) a moving average (MA) is applied to the resulting features vectors and then used as inputs to SVM to train and test. We test the method on EEG signals during two datasets: normal and epileptics, without and with using MA to compare results. Three parameters are evaluated such as sensitivity, specificity, and accuracy to test the performances of the used methods.

scholarly journals AUTOMATIC IDENTIFICATION OF EPILEPTIC AND BACKGROUND EEG SIGNALS USING FREQUENCY DOMAIN PARAMETERS

2010 ◽  
Vol 20 (02) ◽  
pp. 159-176 ◽  
Author(s):  
OLIVER FAUST ◽  
U. RAJENDRA ACHARYA ◽  
LIM CHOO MIN ◽  
BERNHARD H. C. SPUTH
Keyword(s):  
Support Vector Machine ◽  
Moving Average ◽  
Gaussian Mixture ◽  
Power Density Spectrum ◽  
Estimation Methods ◽  
Support Vector ◽  
Automatic Identification ◽  
Spectrum Estimation ◽  
Classification Rate ◽  
Density Spectrum

The analysis of electroencephalograms continues to be a problem due to our limited understanding of the signal origin. This limited understanding leads to ill-defined models, which in turn make it hard to design effective evaluation methods. Despite these shortcomings, electroencephalogram analysis is a valuable tool in the evaluation of neurological disorders and the evaluation of overall cerebral activity. We compared different model based power spectral density estimation methods and different classification methods. Specifically, we used the autoregressive moving average as well as from Yule-Walker and Burg's methods, to extract the power density spectrum from representative signal samples. Local maxima and minima were detected from these spectra. In this paper, the locations of these extrema are used as input to different classifiers. The three classifiers we used were: Gaussian mixture model, artificial neural network, and support vector machine. The classification results are documented with confusion matrices and compared with receiver operating characteristic curves. We found that Burg's method for spectrum estimation together with a support vector machine classifier yields the best classification results. This combination reaches a classification rate of 93.33%, the sensitivity is 98.33% and the specificy is 96.67%.

Fault Diagnosis of a Wind Turbine Benchmark via Statistical and Support Vector Machine

2018 ◽  
Vol 37 ◽  
pp. 29-42 ◽  
Author(s):  
Abderrahmane Mokhtari ◽  
Mohammed Belkheiri
Keyword(s):  
Support Vector Machine ◽  
Fault Detection ◽  
Wind Turbine ◽  
Moving Average ◽  
Fault Detection And Isolation ◽  
Support Vector ◽  
Svm Classifier ◽  
Actuator Faults ◽  
Active System ◽  
Benchmark Model

This paper addresses the problem of fault detection and isolation (FDI) in wind turbine benchmark model using data driven and multi-class support vector machine (SVM) approach. Since, the fault detection is fundamental for any active system, isolation is similarly vital, and identification is decisive for fault reconfiguration as well as maintenance addition to monitoring purposes. The need for man-made dynamic system to work automatically when sensor, actuator, or system faults occur was constantly developed in order to increase reliability and decrease unavailability and maintenance costs. The key step of our approach based on extraction of mean features from sensors measurements by applying the statistical methods such as moving standard deviation and the exponential weighted moving average (EWMA). The fault detection step is invoked later based on the multi-class SVM classifier that decides the presence or not of the fault. Another important contribution of this paper is the simulation of combined sensor and actuator faults simultaneously for the first time in wind turbine benchmark model. The FDI performances are illustrated through simulation study for seven different scenario tests. The results demonstrate clearly the effectiveness of statistical and SVM approach to detect and isolate single, multiple sensor and actuator faults and outperforms many results reported in the literature for solving this problem.

scholarly journals Forecasting the All-Weather Short-Term Metro Passenger Flow Based on Seasonal and Nonlinear LSSVM

2021 ◽  
Vol 33 (2) ◽  
pp. 217-231
Author(s):  
Xin Huang ◽  
Yimin Wang ◽  
Peiqun Lin ◽  
Heng Yu ◽  
Yue Luo
Keyword(s):  
Support Vector Machine ◽  
Short Term Memory ◽  
Moving Average ◽  
Parallel Architecture ◽  
Nonlinear Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Passenger Flow ◽  
Organization Strategy ◽  
Auto Regressive

Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and simultaneously help traffic operators develop a passenger organization strategy. However, short-term passenger flow prediction needs to consider many factors, and the results of the existing models for short-term subway passenger flow forecasting are often unsatisfactory. Along this line, we propose a parallel architecture, called the seasonal and nonlinear least squares support vector machine (SN-LSSVM), to extract the periodicity and nonlinearity characteristics of passenger flow. Various forecasting models, including auto-regressive integrated moving average, long short-term memory network, and support vector machine, are employed for evaluating the performance of the proposed architecture. Moreover, we first applied the method to the Tiyu Xilu station which is the most crowded station in the Guangzhou metro. The results indicate that the proposed model can effectively make all-weather and year-round passenger flow predictions, thus contributing to the management of the station.

scholarly journals Comment on "A hybrid model of self organizing maps and least square support vector machine for river flow forecasting" by Ismail et al. (2012)

2014 ◽  
Vol 18 (7) ◽  
pp. 2711-2714 ◽  
Author(s):  
F. Fahimi ◽  
A. H. El-Shafie
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Water Resource Management ◽  
River Flow ◽  
Moving Average ◽  
Least Square ◽  
Support Vector ◽  
Self Organizing Map ◽  
River Flow Forecasting ◽  
Self Organizing

Abstract. Without a doubt, river flow forecasting is one of the most important issues in water engineering field. There are lots of forecasting techniques that have successfully been utilized by previously conducted studies in water resource management and water engineering. The study of Ismail et al. (2012), which was published in the journal Hydrology and Earth System Sciences in 2012, was a valuable piece of research that investigated the combination of two effective methods (self-organizing map and least squares support vector machine) for river flow forecasting. The goal was to make a comparison between the performances of self organizing map and least square support vector machine (SOM-LSSVM), autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and least squares support vector machine (LSSVM) models for river flow prediction. This comment attempts to focus on some parts of the original paper that need more discussion. The emphasis here is to provide more information about the accuracy of the observed river flow data and the optimum map size for SOM mode as well.

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