Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Forest

Kumar, Manish; Thenmozhi, M.

doi:10.2139/ssrn.876544

Cited by 158 publications

(120 citation statements)

References 40 publications

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“…Most of the studies focusing on time series forecasting aim at proposing a forecasting method based on its performance (compared to other methods, usually a few), when applied within a small number of case studies (e.g., [8,14,26,28,30]). Recognizing this specific fact and aiming at providing a tangible contribution in time series forecasting using RF, we have conducted an extensive set of computational experiments.…”

Section: Discussionmentioning

confidence: 99%

“…Regression using RF can be implemented for time series forecasting purposes. Representative applications can be found in many scientific fields including these of engineering [23,24], environmental and geophysical sciences [25][26][27], financial studies [28,29], and medicine [30], with varying performance. Furthermore, small datasets are used in these applications; therefore, the results cannot be generalized.…”

Section: Time Series Forecasting and Random Forestsmentioning

confidence: 99%

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Variable Selection in Time Series Forecasting Using Random Forests

2017

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Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA) models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Time Series Forecasting and Random Forestsmentioning

confidence: 99%

Variable Selection in Time Series Forecasting Using Random Forests

2017

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“…Forecasting ICP for k time steps with less error rate can be regarded as a really satisfactory result, even in comparison with the results achieved in other areas of interest [34,35]. This is possible mainly due to recent advances in data pre-processing, like preparing the dynamic sliding window for short-term and long-term forecasting.…”

Section: Discussionmentioning

confidence: 96%

Intracranial Pressure Forecasting in Children Using Dynamic Averaging of Time Series Data

et al. 2018

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Increased Intracranial Pressure (ICP) is a serious and often life-threatening condition. If the increased pressure pushes on critical brain structures and blood vessels, it can lead to serious permanent problems or even death. In this study, we propose a novel regression model to forecast ICP episodes in children, 30 min in advance, by using the dynamic characteristics of continuous intracranial pressure, vitals and medications during the last two hours. The correlation between physiological parameters, including blood pressure, respiratory rate, heart rate and the ICP, is analyzed. Linear regression, Lasso regression, support vector machine and random forest algorithms are used to forecast the next 30 min of the recorded ICP. Finally, dynamic features are created based on vitals, medications and the ICP. The weak correlation between blood pressure and the ICP (0.2) is reported. The Root-Mean-Square Error (RMSE) of the random forest model decreased from 1.6 to 0.89% by using the given medication variables in the last two hours. The random forest regression gave an accurate model for the ICP forecast with 0.99 correlation between the forecast and experimental values.

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“…In stead of a single method, the traders need to use various forecasting techniques to gain multiple signals and more information about the future of the markets. Kumar & Thenmozhi (2006) collected five different approaches including SVM, Random forecast, Neural network, Logit and LDA to predict Indian stock index movement based on economic variable indicators. From the comparison, the SVM outperformed the others in forecasting S&P CNX NIFTY index direction as the model does not require any priori assumptions on data property and its algorithm results global optimal solution which is unique.…”

Section: Introductionmentioning

confidence: 99%