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Prediction of Temperature Time Series Based on Wavelet Transform and Support Vector Machine
2012
Journal of Computers
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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
2010
Econometric Reviews
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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
2012
Procedia Engineering
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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
2017
Journal of Mathematics and Informatics
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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
2021
Complexity
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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
2015
International Journal of Computer Applications
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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
2016
International Journal of Hybrid Information Technology
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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
2018
International Journal for Research in Applied Science and Engineering Technology
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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
2014
International Journal of Computer Applications
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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
2013
Open Automation and Control Systems Journal
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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
2021
International Journal for Research in Applied Science and Engineering Technology
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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)
2015
American Journal of Applied Mathematics
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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
2015
Mobile Information Systems
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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
2021
International Journal for Research in Applied Science and Engineering Technology
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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
2021
PeerJ Computer Science
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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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