Time Series Financial Market Forecasting Based On Support Vector Regression Algorithm

Hasibuan, Doli; Jaya, Indra Kelana; Rumahorbo, Benget; Naibaho, Jimmy; Napitupulu, Junika; Rajagukguk, Edward

doi:10.1109/icosnikom48755.2019.9111533

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…MLR was used as a baseline method, as it establishes a linear relationship between the input features and output features. The SVR method produces a classical model that provides a nonlinear solution by mapping input features into a higher-dimensional feature space [30]. As the most commonly used mapping kernel, the radial basis function (RBF) was used in this work to establish the SVR model.…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Machine learning approach for predicting post-intubation hemodynamic instability (PIHI) index values: towards enhanced perioperative anesthesia quality and safety

Te,

Zhu,

et al. 2024

BMC Anesthesiol

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Background Adequate preoperative evaluation of the post-intubation hemodynamic instability (PIHI) is crucial for accurate risk assessment and efficient anesthesia management. However, the incorporation of this evaluation within a predictive framework have been insufficiently addressed and executed. This study aims to developed a machine learning approach for preoperatively and precisely predicting the PIHI index values. Methods In this retrospective study, the valid features were collected from 23,305 adult surgical patients at Peking Union Medical College Hospital between 2012 and 2020. Three hemodynamic response sequences including systolic pressure, diastolic pressure and heart rate, were utilized to design the post-intubation hemodynamic instability (PIHI) index by computing the integrated coefficient of variation (ICV) values. Different types of machine learning models were constructed to predict the ICV values, leveraging preoperative patient information and initiatory drug infusion. The models were trained and cross-validated based on balanced data using the SMOTETomek technique, and their performance was evaluated according to the mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and R-squared index (R2). Results The ICV values were proved to be consistent with the anesthetists’ ratings with Spearman correlation coefficient of 0.877 (P < 0.001), affirming its capability to effectively capture the PIHI variations. The extra tree regression model outperformed the other models in predicting the ICV values with the smallest MAE (0.0512, 95% CI: 0.0511–0.0513), RMSE (0.0792, 95% CI: 0.0790–0.0794), and MAPE (0.2086, 95% CI: 0.2077–0.2095) and the largest R2 (0.9047, 95% CI: 0.9043–0.9052). It was found that the features of age and preoperative hemodynamic status were the most important features for accurately predicting the ICV values. Conclusions Our results demonstrate the potential of the machine learning approach in predicting PIHI index values, thereby preoperatively informing anesthetists the possible anesthetic risk and enabling the implementation of individualized and precise anesthesia interventions.

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Section: Machine Learning Modelsmentioning

confidence: 99%

Machine learning approach for predicting post-intubation hemodynamic instability (PIHI) index values: towards enhanced perioperative anesthesia quality and safety

Te,

Zhu,

et al. 2024

BMC Anesthesiol

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“…There is relatively limited existing literature on the topic of optimal execution of split trade orders using learning techniques, with more papers covering stock forecasting by means of different techniques like neural networks [2] or deep learning [3], support vector machines [4], [5], adaptive line combiners [6] or local data-based techniques [7]. There are however some interesting articles in the field of trade execution optimization such as for instance [8].…”

Section: Introductionmentioning

confidence: 99%

Receding Horizon Optimization of Large Trade Orders

et al. 2021

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The optimal execution of stock trades is a relevant and interesting problem as it is key in maximizing profits and reducing risks when investing in the stock market. In the case of large orders, the problem becomes even more complex as the impact of the order in the market has to be taken into account. The usual solution is to split large orders into a set of smaller suborders that must be executed within a prescribed time window. This leads to the problem of deciding when in the time window execute each suborder. There are popular ways of executing the trading of these split orders like those which try to track the "Time Weighted Average Price" and the "Volume Weighted Average Price", usually called TWAP and VWAP orders. This paper presents a strategy to optimize the splitting of large trade orders over a given time window. The strategy is based on the solution of an optimization problem that is applied following a receding horizon approach. This approach reduces the impact of prediction errors due to the uncertain market dynamics, by using new values of the price time series as they are available as time goes on. Suborder size constraints are taken into account in both market and limit orders. The strategy relies on price and traded volume forecast but it is independent of the prediction technique used. The performance index weighs not only the financial cost of the suborders, but also the impact on the market and the forecasting accuracy. A tailored optimization algorithm is proposed for efficiently solving the corresponding optimization problem. Most of the computations of the algorithm can be parallelized. Finally, the proposed approach has been tested through a case study composed by stocks of the Chinese A-share market.INDEX TERMS algorithmic trading, receding horizon optimization, large stock orders, limit orders, TWAP, VWAP.

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Time Series Financial Market Forecasting Based On Support Vector Regression Algorithm

Cited by 2 publications

References 14 publications

Machine learning approach for predicting post-intubation hemodynamic instability (PIHI) index values: towards enhanced perioperative anesthesia quality and safety

Machine learning approach for predicting post-intubation hemodynamic instability (PIHI) index values: towards enhanced perioperative anesthesia quality and safety

Receding Horizon Optimization of Large Trade Orders

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