scholarly journals Prediction of water quality time series data based on least squares support vector machine

2012 ◽  
Vol 31 ◽  
pp. 1194-1199 ◽  
Author(s):  
Guohua Tan ◽  
Jianzhuo Yan ◽  
Chen Gao ◽  
Suhua Yang
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Support Vector Machine ◽  
Least Squares ◽  
Time Series Data ◽  
Series Data ◽  
Support Vector

scholarly journals Prediction of COVID-19 corona virus pandemic based on time series data using support vector machine

2020 ◽  
Vol 23 (8) ◽  
pp. 1583-1597
Author(s):  
Vijander Singh ◽  
Ramesh Chandra Poonia ◽  
Sandeep Kumar ◽  
Pranav Dass ◽  
Pankaj Agarwal ◽  
...  
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Time Series Data ◽  
Series Data ◽  
Support Vector ◽  
Corona Virus

scholarly journals Efficient time series data classification using sliding window technique based improved association rule mining with enhanced support vector machine

2018 ◽  
Vol 7 (3.3) ◽  
pp. 218 ◽  
Author(s):  
D Senthil ◽  
G Suseendran
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Association Rule ◽  
Association Rule Mining ◽  
Time Series Data ◽  
Sliding Window ◽  
Series Data ◽  
Support Vector ◽  
Rule Mining ◽  
Window Technique

Time series analysis is an important and complex problem in machine learning and statistics. In the existing system, Support Vector Machine (SVM) and Association Rule Mining (ARM) is introduced to implement the time series data. However it has issues with lower accuracy and higher time complexity. Also it has issue with optimal rules discovery and segmentation on time series data. To avoid the above mentioned issues, in the proposed research Sliding Window Technique based Improved ARM with Enhanced SVM (SWT-IARM with ESVM) is proposed. In the proposed system, the preprocessing is performed using Modified K-Means Clustering (MKMC). The indexing process is done by using R-tree which is used to provide faster results. Segmentation is performed by using SWT and it reduces the cost complexity by optimal segments. Then IARM is applied on efficient rule discovery process by generating the most frequent rules. By using ESVM classification approach, the rules are classified more accurately.  

scholarly journals A Novel Time Series Prediction Approach Based on a Hybridization of Least Squares Support Vector Regression and Swarm Intelligence

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Nhat-Duc Hoang ◽  
Anh-Duc Pham ◽  
Minh-Tu Cao
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Support Vector Regression ◽  
Time Series Data ◽  
Time Series Prediction ◽  
Hybrid Approach ◽  
Series Data ◽  
Support Vector ◽  
Promising Alternative ◽  
Prediction Approach

This research aims at establishing a novel hybrid artificial intelligence (AI) approach, named as firefly-tuned least squares support vector regression for time series prediction(FLSVRTSP). The proposed model utilizes the least squares support vector regression (LS-SVR) as a supervised learning technique to generalize the mapping function between input and output of time series data. In order to optimize the LS-SVR’s tuning parameters, theFLSVRTSPincorporates the firefly algorithm (FA) as the search engine. Consequently, the newly construction model can learn from historical data and carry out prediction autonomously without any prior knowledge in parameter setting. Experimental results and comparison have demonstrated that theFLSVRTSPhas achieved a significant improvement in forecasting accuracy when predicting both artificial and real-world time series data. Hence, the proposed hybrid approach is a promising alternative for assisting decision-makers to better cope with time series prediction.

scholarly journals Peramalan Arus Kas dengan Pendekatan Time Series Menggunakan Support Vector Machine

2021 ◽  
Vol 4 (1) ◽  
pp. 34
Author(s):  
Bella Audina ◽  
Mohamat Fatekurohman ◽  
Abduh Riski
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Cash Flow ◽  
Time Series Data ◽  
Optimal Parameter ◽  
Free Cash Flow ◽  
Series Data ◽  
Support Vector ◽  
Operating Income ◽  
Financial Condition

<p>Cash flow is a form of financial report that is used as a measure of the company success in the investment world. So that companies need to forecast the cash flow to manage their finances. Statistics can be applied for the forecasting of cash flow using the <em>Support Vector Machine </em>(SVM) method on the time series data. The aim of this research is to determine the optimal parameter pair model of the <em>Radial Basic Function</em> kernel and to obtain the forecasting results of cash flow using the SVM method on the time series data. The independent variable is needed the data on cash flow from operating income, expenditure and investment expenditure, sum of all cash flow. While the dependent variable is the financial condition based on the <em>Free Cash Flow</em>. The result of this research is a model with the best parameter pairs of the SVM tuning results with the greatest accuracy that is 75%, 82%, 88%, 64% and the forecasting financial condition of PT Cakrawala for the next 16 months.</p><p><strong>Keywords: </strong>cash flow, forecasting, time series, support vector machine.</p>

scholarly journals Fault Detection for Turbine Engine Disk Based on Adaptive Weighted One-Class Support Vector Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jiusheng Chen ◽  
Xingkai Xu ◽  
Xiaoyu Zhang
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Fault Detection ◽  
Data Stream ◽  
Time Series Data ◽  
Series Data ◽  
Support Vector ◽  
Turbine Engine ◽  
Detection Model ◽  
Redundant Data

Fault detection for turbine engine components is becoming increasingly important for the efficient running of commercial aircraft. Recently, the support vector machine (SVM) with kernel function is the most popular technique for monitoring nonlinear processes, which can better handle the nonlinear representation of fault detection of turbine engine disk. In this paper, an adaptive weighted one-class SVM-based fault detection method coupled with incremental and decremental strategy is proposed, which can efficiently solve the time series data stream drifting problem. To update the efficient training of the fault detection model, the incremental strategy based on the new incoming data and support vectors is proposed. The weight of the training sample is updated by the variations of the decision boundaries. Meanwhile, to increase the calculating speed of the fault detection model and reduce the redundant data, the decremental strategy based on the k-nearest neighbor (KNN) is adopted. Based on time series data stream, numerical simulations are conducted and the results validated the superiority of the proposed approach in terms of both the detection performance and robustness.

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