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Prediction of Temperature Time Series Based on Wavelet Transform and Support Vector Machine

Xiaohong Liu, Shujuan Yuan, Li Li
2012 Journal of Computers  
To predict the time series, a model combining the wavelet transform and support vector machine is set up.  ...  The prediction precision of the new model is higher than that of the SVM model and the artificial neural network model for many processes, such as runoff, precipitation, temperature.  ...  In the paper, Firstly, wavelet coefficient on different time scales of time-series is regressed by support vector machine.  ... 
doi:10.4304/jcp.7.8.1911-1918 fatcat:z7dsjbsjpjdjxk5ws5yxdfbtfy

An Empirical Comparison of Machine Learning Models for Time Series Forecasting

Nesreen K. Ahmed, Amir F. Atiya, Neamat El Gayar, Hisham El-Shishiny
2010 Econometric Reviews  
In addition to model comparisons, we have tested different preprocessing methods and have shown that they have different impacts on the performance. 1  ...  CART regression trees, support vector regression, and Gaussian processes.  ...  This work is part of the Data Mining for Improving Tourism Revenue in Egypt research project within the Egyptian Data Mining and Computer Modeling Center of Excellence.  ... 
doi:10.1080/07474938.2010.481556 fatcat:s3bjxi3hivdr7mgfgwirwi6neu

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

Guohua Tan, Jianzhuo Yan, Chen Gao, Suhua Yang
2012 Procedia Engineering  
Experimental results show that the small sample case with noise, least squares support vector machine method is better than multi-layer BP and RBF neural network, to better meets the requirements of water  ...  the BP network and RBF network prediction.  ...  Acknowledgements The data of Beijing Water Authority, and the matlab experiment framework used LSSVMLab matlab toolbox download from http://www.esat.kuleuven.be/sista/lssvmlab/,a special thanks here.  ... 
doi:10.1016/j.proeng.2012.01.1162 fatcat:rqr65gq5rjfyvnnqz4vuv3arfm

A Survey of Stock Forecasting Model Based on Artificial Intelligence Algorithm

Jia-Xuan Deng, Gui Gan
2017 Journal of Mathematics and Informatics  
This paper describes 14 advanced neural networks and support vector machine forecasting techniques at home and abroad, analyzes and summarizes the characteristics and key points of each forecasting method  ...  a model of rough set attribute reduction and neural network based on this genetic algorithm.  ...  Based on the experimental results, it was found that the performance of CMAC NN scheme was superior to robustness evaluation and support vector regression (SVR) and back propagation neural network (BPNN  ... 
doi:10.22457/jmi.v7a9 fatcat:cquqsq2jqvfxdk3sjmx33s6olq

Foreign Trade Export Forecast Based on Fuzzy Neural Network

Yang Liu, Zhihan Lv
2021 Complexity  
export forecast, introduces the whole process of the establishment of the fuzzy neural network forecasting model in detail, and predicts the change interval of foreign trade exports.  ...  Subsequently, the related concepts and principles of artificial neural network and fuzzy theory are explained, the types and training algorithms of the fuzzy neural network are introduced, and the neural  ...  , support vector machine (SVM), neural network-based methods, and combined prediction methods.  ... 
doi:10.1155/2021/5523222 fatcat:dcvm5wgdffcg7hhe6hioyx7bnm

Study of Time Series Data Mining for the Real Time Hydrological Forecasting: A Review

Satanand Mishra, C. Saravanan, V. K. Dwivedi
2015 International Journal of Computer Applications  
Researchers are developed models for runoff forecasting using the data mining tools and techniques like regression analysis, clustering, artificial neural network (ANN), and support vector machine (SVM  ...  The wide use of hydrological time series data has initiated a great deal of research and development attempts in the field of data mining.  ...  Mamat M and Samad A.S. compares the performance of Radial Basis Function and Support Vector Regression in time series forecasting.  ... 
doi:10.5120/20692-3581 fatcat:v3vfg7a4wfgwxfdgxdgvlhvrfq

Predict Time Series with Multiple Artificial Neural Networks

Fei Li, Jin Liu, Lei Kong
2016 International Journal of Hybrid Information Technology  
In order to solve this problem, this paper presents a multiple artificial neural networks prediction method, the method can significantly improve the accuracy of both single time series and multiple time  ...  In the literature, many works were reported to extend different architecture of artificial neural networks to work with time series prediction.  ...  Artificial Neural Networks (ANNs) and Support Vector Machines. Linear Model One of the widely used linear prediction model is auto regressive (AR) model.  ... 
doi:10.14257/ijhit.2016.9.7.28 fatcat:bk674755i5heln2nyndj624tmm

An Efficient AI Model for Financial Market Prediction Optimized by SVR

Sonalika Dash
2018 International Journal for Research in Applied Science and Engineering Technology  
In this paper we have proposed a hybrid model using Multi Layer Perceptron (MLP) optimized by Support Vector Regression Algorithm (SVR) to predict the JSPL stock data.  ...  Stock market is dynamic in nature. So prediction of the stock index accurately is a tedious task.  ...  Proposed Hybrid Prediction Model The proposed Prediction model works on MLP as basic network and optimized by SVR to give better result. The model works on normalized time series data.  ... 
doi:10.22214/ijraset.2018.5307 fatcat:g7xx2ekryzho3ajfqdygfoeeyu

An Efficient Approach of Artificial Neural Network in Runoff Forecasting

Satanand Mishra, Prince Gupta, S. K. Pandey, J. P. Shukla
2014 International Journal of Computer Applications  
This survey paper focused on data mining technique based on artificial neural network and its application in runoff forecasting.  ...  The long-term and short-term forecasting model was developed for runoff forecasting using various approaches of Artificial Neural Network techniques.  ...  APPROACHES OF ARTIFICIAL NEURAL NETWORK IN HYDROLOGICAL PREDICTION Forecasting is one of the crucial research topics in the analysis of hydrological time series data.  ... 
doi:10.5120/16003-4991 fatcat:r4d2vpehofeezpelbb2hvljsai

Combination Model for Short-Term Load Forecasting

Qingming Chen
2013 Open Automation and Control Systems Journal  
In this approach we used regression to model the trend and used neural network for calculating predicted values and errors.  ...  And to prove the effectiveness of the model, support vector machines(SVM) algorithm was used to compare with the result of combination model.  ...  SVM Regression Theory In this section, we briefly introduce support vector regression (SVR) which can be used for time series prediction.  ... 
doi:10.2174/1874444301305010124 fatcat:7jl4t4xhobe6lobb4epgbzi3re

A Neural Network Model for Wildfire Scale Prediction using Meteorological Factors

Lavanya I
2021 International Journal for Research in Applied Science and Engineering Technology  
It has a large amount of forest. Forest fires have a long-term impact on the climate because they contribute to deforestation and global warming, which is one of the main causes of the phenomenon.  ...  This research employs Back Propagation Neural Network (BPNN) and Recurrent Neural Network (RNN) models with meteorological parameters as inputs to anticipate forest fires as a means of safeguarding forest  ...  While traditional deep neural networks assume that inputs and outputs are independent, the performance of recurrent neural networks depends on the previous elements of the series [12].  ... 
doi:10.22214/ijraset.2021.35258 fatcat:7itnawdeczev7mgecpn4ifwnga

Study on Financial Time Series Prediction Based on Phase Space Reconstruction and Support Vector Machine (SVM)

Hong Zhang
2015 American Journal of Applied Mathematics  
Analyzing and forecasting the financial market based on the theory of phase space reconstruction of support vector regression.  ...  Experiments show that the theory of phase space reconstruction based on support vector regression has a certain degree of predictive ability of market value at risk.  ...  nonlinear characteristics of Financial Time Series has been widely recognized, Therefore, based on the nonlinear Time Series modeling principle, studies the application of neural network and support vector  ... 
doi:10.11648/j.ajam.20150303.16 fatcat:rvyuzs6ppbbrhjcbgekwhiu4ty

Support Vector Regression Based Indoor Location in IEEE 802.11 Environments

Ke Shi, Zhenjie Ma, Rentong Zhang, Wenbiao Hu, Hongsheng Chen
2015 Mobile Information Systems  
In this paper, we present a new 802.11-based indoor positioning method using support vector regression (SVR), which consists of offline training stage and online location stage.  ...  To address this issue, data filtering rules obtained through statistical analysis are applied at offline training stage to improve the quality of training samples and thus improve the quality of prediction  ...  Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper. Acknowledgments  ... 
doi:10.1155/2015/295652 fatcat:fhxxzfnyw5fk7ldanc5uqgc354

Stock Market Prediction using Machine Learning

Prof. Gowrishankar B S
2021 International Journal for Research in Applied Science and Engineering Technology  
Stock market is one of the most complicated and sophisticated ways to do business.  ...  The use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values.  ...  Predicting the stock market is a challenging feat, owing to the near similarity of a stock time series to a normal distribution.  ... 
doi:10.22214/ijraset.2021.35755 fatcat:svspoa3cwfdm3ev42yyoz3adfu

News sensitive stock market prediction: literature review and suggestions

Shazia Usmani, Jawwad A. Shamsi
2021 PeerJ Computer Science  
Furthermore, it highlights the significance of deep neural network based prediction techniques to capture the hidden relationship between textual and numerical data.  ...  Stock market prediction is a challenging task as it requires deep insights for extraction of news events, analysis of historic data, and impact of news events on stock price trends.  ...  The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  ... 
doi:10.7717/peerj-cs.490 pmid:34013029 pmcid:PMC8114814 fatcat:wuxzdb2avzh73nsbmwfdjybcte
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