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Autoregressive short-term prediction of turning points using support vector regression
[article]
2012
arXiv
pre-print
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Our method relies on a known turning point indicator, a Fourier enriched representation of price histories, and support vector regression. ...
This work is concerned with autoregressive prediction of turning points in financial price sequences. ...
Predicting turning points with Support Vector Regression In this section we describe our application of Support Vector Regression (SVR) to predict the TP oscillator (presented in Section 3.3). ...
arXiv:1209.0127v2
fatcat:rbdbn75sgfhnrmetat7bt256py
The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach
2016
Journal of Forecasting
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We employ two nonlinear methodologies: the econometric Least Absolute Shrinkage and Selection Operator (LASSO) and the machine learning Support Vector Regression (SVR) method. ...
In order to evaluate the contribution of the term-spread in inflation forecasting in different time periods, we measure the out-of-sample forecasting performance of all models using rolling window regressions ...
Methodology and Data
Support Vector Regression The Support Vector Regression is a direct extension of the classic Support Vector Machine algorithm. ...
doi:10.1002/for.2417
fatcat:isz52uj2zbgondjjdofxlvzrbm
Blockchain and Cryptocurrencies
2020
Journal of Risk and Financial Management
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Cryptocurrencies are essentially digital currencies that use blockchain technology and cryptography to facilitate secure and anonymous transactions. ...
The aim of this Special Issue is to provide a collection of papers from leading experts in the area of blockchain and cryptocurrencies. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/jrfm13100227
fatcat:u6kjk2mp3vhkthy4zho7eown2e
Short-Term Electricity Generation Forecasting Using Machine Learning Algorithms: A Case Study of the Benin Electricity Community (C.E.B)
2021
TH Wildau Engineering and Natural Sciences Proceedings
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This work proposes the deployment of three univariate Machine Learning models: Support Vector Regression, Multi-Layer Perceptron, and the Long Short-Term Memory Recurrent Neural Network to predict the ...
In order to validate the performance of these different methods, against the Autoregressive Integrated Mobile Average and Multiple Regression model, performance metrics were used. ...
This process is interrupted as soon as the global error is estimated to be sufficient
Support Vector Regression (SVR) Support Vector Regression (SVR) is an adaptation of Support Vector Machines (SVM) ...
doi:10.52825/thwildauensp.v1i.25
fatcat:qojcrfztqrelzjb6ashmrloeo4
Multi-Step Time Series Forecasting with an Ensemble of Varied Length Mixture Models
2018
International Journal of Neural Systems
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A combination of short and long VLM models and an ensemble of them are proposed to further enhance the prediction performance. ...
Multi-step or long-term prediction is difficult and challenging due to the lack of information and uncertainty or error accumulation. ...
To turn SOM into time series models, each neuron has to cast a regressive model. ...
doi:10.1142/s0129065717500538
pmid:29297261
fatcat:zoenqifi5jgbriddt2p2d2karu
Weather Forecasting Using Merged Long Short-Term Memory Model (LSTM) and Autoregressive Integrated Moving Average (ARIMA) Model
2018
Journal of Computer Science
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The aim of these study is to analyze intermediate variables and do the comparison of visibility forecasting by using Autoregressive Integrated Moving Average (ARIMA) and Long Short Term Memory (LSTM) Model ...
variable weather data as moderating variables such as temperature, dew point and humidity. ...
Acknowledgment This research is supported by Doctoral of Computer Science, Bina Nusantara University. ...
doi:10.3844/jcssp.2018.930.938
fatcat:zi42lanqbfbu7nxqql4syamvxm
Short-term electric load forecasting using computational intelligence methods
2013
2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce several methods for short-term electric load forecasting. ...
All the presented methods stem from computational intelligence techniques: Random Forest, Nonlinear Autoregressive Neural Networks, Evolutionary Support Vector Machines and Fuzzy Inductive Reasoning. ...
regression techniques, such as autoregressive moving average [2] or autoregressive distributed-lag models, among others, that have traditionally been used in short-term electric load forecasting ( ...
doi:10.1109/fuzz-ieee.2013.6622523
dblp:conf/fuzzIEEE/JuradoPNMC13
fatcat:wyskiz7ogjegncogi45cpay3yu
Short-term Forecasting of Intermodal Freight Using ANNs and SVR: Case of the Port of Algeciras Bay
2016
Transportation Research Procedia
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In this paper, two forecasting-models are presented and compared to predict the freight volume. The models developed and tested are based on Artificial Neural Networks and Support Vector Machines. ...
The use of accurate prediction tools is an issue that awakens a lot of interest among transport researchers. ...
The forecasting-models are Artificial Neural Network and Support Vector Machines for Regression. Each model is composed using different values of their parameters. ...
doi:10.1016/j.trpro.2016.12.015
fatcat:72cpckrw2fgtpnplnj33l42mly
Robust Building Energy Load Forecasting Using Physically-Based Kernel Models
2018
Energies
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We evaluate our method with three field datasets from two university campuses (Carnegie Mellon University and Stanford University) for both short-and long-term load forecasting. ...
The GPR is a non-parametric regression method that models the data as a joint Gaussian distribution with mean and covariance functions and forecast using the Bayesian updating. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/en11040862
fatcat:f2onzo53vrhm5fxvclbtknqxxi
Application of Bayesian Vector Autoregressive Model in Regional Economic Forecast
2021
Complexity
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The Bayesian vector autoregressive (BVAR) model introduces the statistical properties of variables as the prior distribution of the parameters into the traditional vector autoregressive (VAR) model, which ...
The BVAR model established in this paper can overcome the problem of short time series data by using prior statistical information. ...
Moreover, the BVAR model is more accurate in predicting the turning point of economic growth in 2019. ...
doi:10.1155/2021/9985072
doaj:e7317571e4254c539097513141b35388
fatcat:jlp3kjgrtzgjvhxtlsbivrvify
Prediction of Wind Speed Using Hybrid Techniques
2020
Energies
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The methodology relies on the use of three non-parametric techniques: Least-squares support vector machines, empirical mode decomposition, and the wavelet transform. ...
Experiments using a matlab implementation showed that the least-squares support vector machine using data as a single time series outperformed the other combinations, obtaining the least root mean square ...
Acknowledgments: The authors thank Universidad del Norte for the support given through the Energy Strategic Area Program and for the availability of Renewable energy Laboratory, UniGrid. ...
doi:10.3390/en13236284
fatcat:zsvlcvn7cjf7nmvnxn4saeacn4
ELECTRICITY BILL PRICE FORECASTING WITH ARIMA MODEL USING THE CONCEPT OF MACHINE LEARNING
2019
International Journal of Information Systems and Computer Sciences
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principal component analysis is being used. ...
Price forecasting has always played a pivotal role in the success of every institution or life course. Electricity price forecasting is one of the key elements among price forecasting. ...
machines, which are fuzzy support vector machine and fuzzy rough support vector machine respectively [9] . ...
doi:10.30534/ijiscs/2019/33822019
fatcat:osu2ajygw5cb3kveihj6a5l6pi
Using Vector Autoregression Modeling to Reveal Bidirectional Relationships in Gender/Sex-Related Interactions in Mother–Infant Dyads
2020
Frontiers in Psychology
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Vector autoregression (VAR) modeling allows probing bidirectional relationships in gender/sex development and may support hypothesis testing following multi-modal data collection. ...
VAR models demonstrated that infant crawling predicted a subsequently close feedback loop from mothers of boys but a subsequently open-ended, branched response from mothers of girls. ...
Ronald Seifer for sharing the original videotapes with us, Dr. Cynthia Garcia-Coll for her guidance during the early years of this project, and Dr. ...
doi:10.3389/fpsyg.2020.01507
pmid:32848979
pmcid:PMC7419485
fatcat:zm6jf6v2grd2ppgvidpms4fbke
Dynamics of Heteroscedasticity Modelling and Forecasting of Tax Revenue in a Developing Economy: A Review
2020
Regular Issue
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The dynamics of heteroscedasticity in the financial time series (tax revenue) in the domain of technique used to model and predict tax revenue in the emerging economy threw us to this investigation. ...
Thus, we recommend the combination of linear and nonlinear models for both tax revenue and stock exchange data which can minimize the error of heteroscedasticity in the forecasting of tax revenue in a ...
ICA with support vector regression SVR. ...
doi:10.35940/ijmh.c1161.115320
fatcat:zdu6hrq7m5b2xaayq27t7cysje
Support vector machine-based short-term wind power forecasting
2011
2011 IEEE/PES Power Systems Conference and Exposition
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Instead of predicting wind power directly, the proposed model first predicts the wind speed, which is then used to predict the wind power by using the power-wind speed characteristics of the wind turbine ...
Index Terms--Artificial neural network (ANN), radial basis function (RBF), regression, statistical model, support vector machine (SVM), wind power forecasting (WPF) ...
The data points associated with the nonzero coefficients having approximation errors equal to or larger than ε are referred to as support vectors. ...
doi:10.1109/psce.2011.5772573
fatcat:7h3pipwdvze25gvjxcxaoqiiwi
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