A comparison of SVR and NARX in financial time series forecasting

Tas, Engin; Atli, Ayca Hatice

doi:10.1504/ijcee.2022.122835

Cited by 5 publications

(2 citation statements)

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“…Thus, ML approaches can be used to build highperformance SPF systems without expert knowledge. The traditional ML algorithms, such as ANNs, 11,30,31 k-nearest neighbors (KNN), 32,33 support vector machine (SVM), [34][35][36][37][38][39][40] ensemble models, [41][42][43][44][45][46][47] and BN, 48,49 have been successfully and widely used in SPF systems. Table 2 presents articles on SPF based on ML approaches.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Vuong,

Phu,

Van Nguyen

et al. 2024

Science Progress

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We introduce a comprehensive analysis of several approaches used in stock price forecasting, including statistical, machine learning, and deep learning models. The advantages and limitations of these models are discussed to provide an insight into stock price forecasting. Traditional statistical methods, such as the autoregressive integrated moving average and its variants, are recognized for their efficiency, but they also have some limitations in addressing non-linear problems and providing long-term forecasts. Machine learning approaches, including algorithms such as artificial neural networks and random forests, are praised for their ability to grasp non-linear information without depending on stochastic data or economic theory. Moreover, deep learning approaches, such as convolutional neural networks and recurrent neural networks, can deal with complex patterns in stock prices. Additionally, this study further investigates hybrid models, combining various approaches to explore their strengths and counterbalance individual weaknesses, thereby enhancing predictive accuracy. By presenting a detailed review of various studies and methods, this study illuminates the direction of stock price forecasting and highlights potential approaches for further studies refining the stock price forecasting models.

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Section: Machine Learning Approachesmentioning

confidence: 99%

A bibliometric literature review of stock price forecasting: From statistical model to deep learning approach

Vuong,

Phu,

Van Nguyen

et al. 2024

Science Progress

View full text Add to dashboard Cite

show abstract

“…CART predicts the value of a continuous target variable by recursively partitioning the input space, constructing a binary tree with internal nodes representing feature tests, branches corresponding to outcomes, and leaf nodes representing predicted values. The algorithm selects feature-split points to maximize the reduction in sum of squared errors (SSE) for each partition, optimizing the cost function: Support Vector Regression (SVR) is an extension of the Support Vector Machine (SVM) algorithm for regression tasks, which has been widely used in various fields, such as finance (Tas & Atli., 2022), and biology (Batta et al, 2022), due to its ability to handle high-dimensional data and its robustness to noise. SVR employs nonlinear projections to map the input data into a higher-dimensional feature space, where the regression function can be effectively estimated.…”

Section: Classification and Regression Treesmentioning

confidence: 99%