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Autoregressive short-term prediction of turning points using support vector regression [article]

Ran El-Yaniv, Alexandra Faynburd
2012 arXiv   pre-print
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

Vasilios Plakandaras, Periklis Gogas, Theophilos Papadimitriou, Rangan Gupta
2016 Journal of Forecasting  
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

Stephen Chan, Jeffrey Chu, Yuanyuan Zhang, Saralees Nadarajah
2020 Journal of Risk and Financial Management  
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)

Agbassou Guenoupkati, Adekunlé Akim Salami, Mawugno Koffi Kodjo, Kossi Napo
2021 TH Wildau Engineering and Natural Sciences Proceedings  
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

Yicun Ouyang, Hujun Yin
2018 International Journal of Neural Systems  
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

Afan Galih Salman, Yaya Heryadi, Edi Abdurahman, Wayan Suparta
2018 Journal of Computer Science  
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

Sergio Jurado, Juan Peralta, Angela Nebot, Francisco Mugica, Paulo Cortez
2013 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)  
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

J.A. Moscoso-López, I.J. Turias Turias, M.J. Come, J.J. Ruiz-Aguilar, M. Cerbán
2016 Transportation Research Procedia  
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

Anand Prakash, Susu Xu, Ram Rajagopal, Hae Noh
2018 Energies  
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

Jinghao Ma, Yujie Shang, Hongyan Zhang
2021 Complexity  
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

Luis Lopez, Ingrid Oliveros, Luis Torres, Lacides Ripoll, Jose Soto, Giovanny Salazar, Santiago Cantillo
2020 Energies  
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

Anjali Krishnan
2019 International Journal of Information Systems and Computer Sciences  
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

Elizabeth G. Eason, Nicole S. Carver, Damian G. Kelty-Stephen, Anne Fausto-Sterling
2020 Frontiers in Psychology  
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  
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

Jianwu Zeng, Wei Qiao
2011 2011 IEEE/PES Power Systems Conference and Exposition  
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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