Forecasting performance of support vector machine for the Poyang Lake's water level

2014 ◽  
Vol 70 (9) ◽  
pp. 1488-1495 ◽  
Author(s):  
Yingying Lan
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Model Performance ◽  
Optimal Solution ◽  
Kernel Functions ◽  
Support Vector ◽  
Hydrologic Time Series ◽  
Forecasting Performance ◽  
Linear Svm

The growth of forecasting models has resulted in the development of an excellent model known as the support vector machine (SVM). SVMs can find a global optimal solution equipped with kernel functions. This research trains and tests the SVM network and constructs the support vector regression prediction model by using hydrologic data. Six hydrologic time series were calculated by different kernel functions (namely, linear, polynomial, radial basis function (RBF)), to determine which kernel is the more suitable hydrologic time series in practice. A new solution is presented to identify the good parameter (C; g) by using grid-search and cross-validation. Results prove that linear SVM is a superior model to polynomial and RBF and produced the most accurate results for modeling hydrologic time series behavior as complex hydrologic phenomena. The case study also shows that the calculation errors were correlated with data characteristics. More stable raw data will result in a more accurate result, whereas more random data will result in a more inaccurate result. Model performance could also be dependent on base data nonlinearity.

Research on time series characteristics of sports training effect based on support vector machine

2020 ◽  
pp. 1-9
Author(s):  
Xiaobing Du
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Model Performance ◽  
Support Vector ◽  
Training Effect ◽  
Complex Environment ◽  
Processing Efficiency ◽  
Feature Extractor ◽  
Construction Model ◽  
Extraction Model

Sports athletes not only exercise fast, but also suffer from the surrounding complex environment. Therefore, the video needs to be sequenced to improve processing efficiency. From the perspective of machine learning, this paper designs a spatial feature extractor based on CNN to extract time series features of sports. Moreover, this paper uses the support vector machine as the basis of the construction model to construct a feature extraction model based on support vector machine and random forest based on different situations. At the same time, this paper collects test data through the sports database and uses the swimming project as an example to analyze the model performance. Finally, the paper verifies the validity of the model by comparing and verifying methods. The research indicates that the proposed method has certain effectiveness and can provide theoretical reference for subsequent related research.

scholarly journals Using a least squares support vector machine to estimate a local geometric geoid model

2014 ◽  
Vol 20 (2) ◽  
pp. 427-443 ◽  
Author(s):  
Szu-Pyng Kao ◽  
Chao-Nan Chen ◽  
Hui-Chi Huang ◽  
Yu-Ting Shen
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Curve Surface ◽  
Optimal Solution ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Geoid Model ◽  
Geoidal Undulation ◽  
Orthometric Heights

In this study, test-region global positioning system (GPS) control points exhibiting known first-order orthometric heights were employed to obtain the points of plane coordinates and ellipsoidal heights by using the real-time GPS kinematic measurement method. Plane-fitting, second-order curve-surface fitting, back-propagation (BP) neural networks, and least-squares support vector machine (LS-SVM) calculation methods were employed. The study includes a discussion on data integrity and localization, changing reference-point quantities and distributions to obtain an optimal solution. Furthermore, the LS-SVM was combined with local geoidal-undulation models that were established by researching and analyzing3 kernel functions. The results indicated that the overall precision of the local geometric geoidal-undulation values calculated using the radial basis function (RBF) and third-order polynomial kernel function was optimal and the root mean square error (RMSE) was approximately ± 1.5 cm. These findings demonstrated that the LS-SVM provides a rapid and practical method for determining orthometric heights and should serve as a valuable academic reference regarding local geoid models.

Acoustic Signal Recognition Based on Time Series Model

2011 ◽  
Vol 189-193 ◽  
pp. 3243-3248
Author(s):  
Yu Quan Cui ◽  
Le Jun Shi ◽  
Yu Wei Fang
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Acoustic Signal ◽  
Principal Component ◽  
Acoustic Signals ◽  
Time Series Model ◽  
Support Vector ◽  
Isometric Transformation ◽  
Transformation Time

Using time series model, isometric transformation time series model and ARTAFIT model, we deal with acoustic signal, obtaining different sets of parameters according to different acoustic signals. We use support vector machine (SVM) to recognize different acoustic signals by analyzing different sets of parameters. When the parameter set is too large, we should first reduce order making use of principal component analysis (PCA), then we can recognize them using support vector machine. In the end, we give a case study, which indicate the results of applying our models are satisfactory.

scholarly journals Forecasting ASEAN Tourist Arrivals in Malaysia using Different Time Series Models

2019 ◽  
Vol 8 (6S3) ◽  
pp. 572-578
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Hybrid Model ◽  
Moving Average ◽  
Percentage Error ◽  
Support Vector ◽  
Time Series Models ◽  
Mode Decomposition ◽  
Asean Country ◽  
Forecasting Performance

In this study three time series models are used for forecasting monthly ASEAN tourist arrivals in Malaysia from January 1999 to December 2015. Brunei, Thailand and Vietnam of ASEAN country selected as case study. This paper compares the forecasting accuracy of seasonal autoregressive integrated moving average (SARIMA), Support Vector Machine (SVM) and Wavelet Support Vector Machine (WSVM) and Empirical Mode Decomposition with Wavelet Support Vector Machine (EMD_WSVM) using root mean square error (RMSE) and mean absolute percentage error (MAPE) criterion. Moreover, correlation test has also been carried out to strengthen decisions, and to check accuracy of various forecasting models. Based on the forecasting performance of all four models, hybrid model SARIMA and EMD_WSVM are found to be best models as compare to single model SVM and hybrid model WSVM.

Simulation and Performance Analysis of Tilted Time Window and Support Vector Machine Based Learning Object Ranking Method

2020 ◽  
Vol 13 (2) ◽  
pp. 153-164
Author(s):  
Narina Thakur ◽  
Deepti Mehrotra ◽  
Abhay Bansal ◽  
Manju Bala
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Time Window ◽  
Optimal Solution ◽  
Similarity Score ◽  
Learning Objects ◽  
Retrieval Algorithm ◽  
Support Vector ◽  
Computationally Efficient ◽  
User Query

Objective: Since the adequacy of Learning Objects (LO) is a dynamic concept and changes in its use, needs and evolution, it is important to consider the importance of LO in terms of time to assess its relevance as the main objective of the proposed research. Another goal is to increase the classification accuracy and precision. Methods: With existing IR and ranking algorithms, MAP optimization either does not lead to a comprehensively optimal solution or is expensive and time - consuming. Nevertheless, Support Vector Machine learning competently leads to a globally optimal solution. SVM is a powerful classifier method with its high classification accuracy and the Tilted time window based model is computationally efficient. Results: This paper proposes and implements the LO ranking and retrieval algorithm based on the Tilted Time window and the Support Vector Machine, which uses the merit of both methods. The proposed model is implemented for the NCBI dataset and MAT Lab. Conclusion: The experiments have been carried out on the NCBI dataset, and LO weights are assigned to be relevant and non - relevant for a given user query according to the Tilted Time series and the Cosine similarity score. Results showed that the model proposed has much better accuracy.

scholarly journals Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features

2021 ◽  
Vol 186 (Supplement_1) ◽  
pp. 445-451
Author(s):  
Yifei Sun ◽  
Navid Rashedi ◽  
Vikrant Vaze ◽  
Parikshit Shah ◽  
Ryan Halter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Real World ◽  
Short Term Memory ◽  
Model Performance ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Continuous Map

ABSTRACT Introduction Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. Materials and Methods Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes. Results Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence. Conclusion We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.

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