Volatility Forecasting Using Support Vector Regression and a Hybrid Genetic Algorithm

Santamaría-Bonfil, Guillermo; Frausto–Solís, Juan; Vázquez-Rodarte, Ignacio

doi:10.1007/s10614-013-9411-x

Cited by 21 publications

(7 citation statements)

References 22 publications

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“…Both the SVM and SVR are used to minimize the error of margin and employ kernel functions for non-separable classes. The results can be improved by optimizing its parameters; in this regard grid and heuristic search are used to get best parameters [27] . SVR for the multidimensional data is mathematically formulated as:

…”

Section: Design Methodology For Covid-19 Predictionmentioning

confidence: 99%

Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM

Shahid

Zameer

Muneeb

2020

Chaos, Solitons & Fractals

489

300

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COVID-19, responsible of infecting billions of people and economy across the globe, requires detailed study of the trend it follows to develop adequate short-term prediction models for forecasting the number of future cases. In this perspective, it is possible to develop strategic planning in the public health system to avoid deaths as well as managing patients. In this paper, proposed forecast models comprising autoregressive integrated moving average (ARIMA), support vector regression (SVR), long shot term memory (LSTM), bidirectional long short term memory (Bi-LSTM) are assessed for time series prediction of confirmed cases, deaths and recoveries in ten major countries affected due to COVID-19. The performance of models is measured by mean absolute error, root mean square error and r2_score indices. In the majority of cases, Bi-LSTM model outperforms in terms of endorsed indices. Models ranking from good performance to the lowest in entire scenarios is Bi-LSTM, LSTM, GRU, SVR and ARIMA. Bi-LSTM generates lowest MAE and RMSE values of 0.0070 and 0.0077, respectively, for deaths in China. The best r2_score value is 0.9997 for recovered cases in China. On the basis of demonstrated robustness and enhanced prediction accuracy, Bi-LSTM can be exploited for pandemic prediction for better planning and management.

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…”

Section: Design Methodology For Covid-19 Predictionmentioning

confidence: 99%

Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM

Shahid

Zameer

Muneeb

2020

Chaos, Solitons & Fractals

489

300

View full text Add to dashboard Cite

show abstract

“…Our findings indicate that the accuracy of volatility forecasts depends substantially on the applied proxy of volatility. This conclusion is important since in most papers concerning the application of SVR models to volatility forecasting, only the squared daily returns (or a moving average of the daily squared returns) have been analyzed (e.g., [22][23][24][25][26][27][28][29][30][31][32][33]). Our results, obtained for the squared daily returns, confirm the conclusion formulated in these studies that SVR can lead to lower forecasting errors than the GARCH models.…”

Section: Discussion Of the Resultsmentioning

confidence: 99%

Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression

2020

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We compare the forecasting performance of the generalized autoregressive conditional heteroscedasticity (GARCH) -type models with support vector regression (SVR) for futures contracts of selected energy commodities: Crude oil, natural gas, heating oil, gasoil and gasoline. The GARCH models are commonly used in volatility analysis, while SVR is one of machine learning methods, which have gained attention and interest in recent years. We show that the accuracy of volatility forecasts depends substantially on the applied proxy of volatility. Our study confirms that SVR with properly determined hyperparameters can lead to lower forecasting errors than the GARCH models when the squared daily return is used as the proxy of volatility in an evaluation. Meanwhile, if we apply the Parkinson estimator which is a more accurate approximation of volatility, the results usually favor the GARCH models. Moreover, it is difficult to choose the best model among the GARCH models for all analyzed commodities, however, forecasts based on the asymmetric GARCH models are often the most accurate. While, in the class of the SVR models, the results indicate the forecasting superiority of the SVR model with the linear kernel and 15 lags, which has the lowest mean square error (MSE) and mean absolute error (MAE) among the SVR models in 92% cases.

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“…We developed a hybrid Forecasting method named SVR-ESAR, which uses GA-SVR and an adjustment phase. The SVR-ESAR Architecture GA-SVR has two phases and is shown in Figure 1: ❖ Phase 1: A Genetic Algorithm adjusts the parameters of an SVR machine and its kernels (linear, rbf, sigmoid) using Grid Search and iteratively improves the F(t) forecast of SVR [10]. The improved forecast F*1(t) in this phase has a MAPE error dependent on the residuals during the training phase.…”

Section: Description Of the Svr-esar Methodsmentioning

confidence: 99%

“…For obtaining good forecasting results, in the quality and stability of SVR, its parameters and the kernel parameters should be tuned. Typically, they are tuned by a grid search, a genetic or other heuristic selects the best parameters [10]. Exponential smoothing (ES) .…”

Section: Introductionmentioning

confidence: 99%

The Hybrid Forecasting Method SVR-ESAR for Covid-19

Frausto–Solís

Vazquez

González-Barbosa

et al. 2020

Preprint

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We know that SARS-Cov2 produces the new COVID-19 disease, which is one of the most dangerous pandemics of modern times. This pandemic has critical health and economic consequences, and even the health services of the large, powerful nations may be saturated. Thus, forecasting the number of infected persons in any country is essential for controlling the situation. In the literature, different forecasting methods have been published attempting to solve the problem. However, a simple and accurate forecasting method is required for its implementation in any part of the world. This paper presents a precise and straightforward forecasting method named SVR-ESAR (Support Vector regression hybridized with the classical Exponential smoothing and ARIMA). We applied this method to the infected time series in four scenarios: the Whole World, China, the US, and Mexico. We compared our results with those of the literature showing the proposed method has the best accuracy.

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Volatility Forecasting Using Support Vector Regression and a Hybrid Genetic Algorithm

Cited by 21 publications

References 22 publications

Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM

Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM

Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression

The Hybrid Forecasting Method SVR-ESAR for Covid-19

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