The Experts below are selected from a list of 58038 Experts worldwide ranked by ideXlab platform
Lijuan Cao - One of the best experts on this subject based on the ideXlab platform.
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Support vector machines experts for Time Series Forecasting
Neurocomputing, 2003Co-Authors: Lijuan CaoAbstract:Abstract This paper proposes using the support vector machines (SVMs) experts for Time Series Forecasting. The generalized SVMs experts have a two-stage neural network architecture. In the first stage, self-organizing feature map (SOM) is used as a clustering algorithm to partition the whole input space into several disjointed regions. A tree-structured architecture is adopted in the partition to avoid the problem of predetermining the number of partitioned regions. Then, in the second stage, multiple SVMs, also called SVM experts, that best fit partitioned regions are constructed by finding the most appropriate kernel function and the optimal free parameters of SVMs. The sunspot data, Santa Fe data sets A, C and D, and the two building data sets are evaluated in the experiment. The simulation shows that the SVMs experts achieve significant improvement in the generalization performance in comparison with the single SVMs models. In addition, the SVMs experts also converge faster and use fewer support vectors.
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application of support vector machines in financial Time Series Forecasting
Omega-international Journal of Management Science, 2001Co-Authors: Francis E. H. Tay, Lijuan CaoAbstract:This paper deals with the application of a novel neural network technique, support vector machine (SVM), in financial Time Series Forecasting. The objective of this paper is to examine the feasibility of SVM in financial Time Series Forecasting by comparing it with a multi-layer back-propagation (BP) neural network. Five real futures contracts that are collated from the Chicago Mercantile Market are used as the data sets. The experiment shows that SVM outperforms the BP neural network based on the criteria of normalized mean square error (NMSE), mean absolute error (MAE), directional symmetry (DS) and weighted directional symmetry (WDS). Since there is no structured way to choose the free parameters of SVMs, the variability in performance with respect to the free parameters is investigated in this study. Analysis of the experimental results proved that it is advantageous to apply SVMs to forecast financial Time Series.
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application of support vector machines in nancial Time Series Forecasting
2001Co-Authors: Lijuan CaoAbstract:This paper deals with the application of a novel neural network technique, support vector machine (SVM), in !nancial Time Series Forecasting. The objective of this paper is to examine the feasibility of SVM in !nancial Time Series Forecasting by comparing it with a multi-layer back-propagation (BP) neural network. Five real futures contracts that are collated from the Chicago Mercantile Market are used as the data sets. The experiment shows that SVM outperforms the BP neural network based on the criteria of normalized mean square error (NMSE), mean absolute error (MAE), directional symmetry (DS) and weighted directional symmetry (WDS). Since there is no structured way to choose the free parameters of SVMs, the variability in performance with respect to the free parameters is investigated in this study. Analysis of the experimental results proved that it is advantageous to apply SVMs to forecast !nancial Time Series. ? 2001 Elsevier Science Ltd. All rights reserved.
Qi Zhang - One of the best experts on this subject based on the ideXlab platform.
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spectral temporal graph neural network for multivariate Time Series Forecasting
arXiv: Learning, 2021Co-Authors: Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Jing Bai, Jie Tong, Qi ZhangAbstract:Multivariate Time-Series Forecasting plays a crucial role in many real-world applications. It is a challenging problem as one needs to consider both intra-Series temporal correlations and inter-Series correlations simultaneously. Recently, there have been multiple works trying to capture both correlations, but most, if not all of them only capture temporal correlations in the Time domain and resort to pre-defined priors as inter-Series relationships. In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate Time-Series Forecasting. StemGNN captures inter-Series correlations and temporal dependencies \textit{jointly} in the \textit{spectral domain}. It combines Graph Fourier Transform (GFT) which models inter-Series correlations and Discrete Fourier Transform (DFT) which models temporal dependencies in an end-to-end framework. After passing through GFT and DFT, the spectral representations hold clear patterns and can be predicted effectively by convolution and sequential learning modules. Moreover, StemGNN learns inter-Series correlations automatically from the data without using pre-defined priors. We conduct extensive experiments on ten real-world datasets to demonstrate the effectiveness of StemGNN. Code is available at this https URL
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spectral temporal graph neural network for multivariate Time Series Forecasting
Neural Information Processing Systems, 2020Co-Authors: Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Jing Bai, Jie Tong, Qi ZhangAbstract:Multivariate Time-Series Forecasting plays a crucial role in many real-world applications. It is a challenging problem as one needs to consider both intra-Series temporal correlations and inter-Series correlations simultaneously. Recently, there have been multiple works trying to capture both correlations, but most, if not all of them only capture temporal correlations in the Time domain and resort to pre-defined priors as inter-Series relationships. In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate Time-Series Forecasting. StemGNN captures inter-Series correlations and temporal dependencies jointly in the spectral domain. It combines Graph Fourier Transform (GFT) which models inter-Series correlations and Discrete Fourier Transform (DFT) which models temporal dependencies in an end-to-end framework. After passing through GFT and DFT, the spectral representations hold clear patterns and can be predicted effectively by convolution and sequential learning modules. Moreover, StemGNN learns inter-Series correlations automatically from the data without using pre-defined priors. We conduct extensive experiments on ten real-world datasets to demonstrate the effectiveness of StemGNN.
Ahmet Murat Ozbayoglu - One of the best experts on this subject based on the ideXlab platform.
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financial Time Series Forecasting with deep learning a systematic literature review 2005 2019
Applied Soft Computing, 2020Co-Authors: Omer Berat Sezer, Mehmet Ugur Gudelek, Ahmet Murat OzbayogluAbstract:Abstract Financial Time Series Forecasting is undoubtedly the top choice of computational intelligence for finance researchers in both academia and the finance industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers have created various models, and a vast number of studies have been published accordingly. As such, a significant number of surveys exist covering ML studies on financial Time Series Forecasting. Lately, Deep Learning (DL) models have appeared within the field, with results that significantly outperform their traditional ML counterparts. Even though there is a growing interest in developing models for financial Time Series Forecasting, there is a lack of review papers that solely focus on DL for finance. Hence, the motivation of this paper is to provide a comprehensive literature review of DL studies on financial Time Series Forecasting implementation. We not only categorized the studies according to their intended Forecasting implementation areas, such as index, forex, and commodity Forecasting, but we also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), and Long-Short Term Memory (LSTM). We also tried to envision the future of the field by highlighting its possible setbacks and opportunities for the benefit of interested researchers.
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financial Time Series Forecasting with deep learning a systematic literature review 2005 2019
2019Co-Authors: Omer Berat Sezer, Mehmet Ugur Gudelek, Ahmet Murat OzbayogluAbstract:Financial Time Series Forecasting is, without a doubt, the top choice of computational intelligence for finance researchers from both academia and financial industry due to its broad implementation areas and substantial impact. Machine Learning (ML) researchers came up with various models and a vast number of studies have been published accordingly. As such, a significant amount of surveys exist covering ML for financial Time Series Forecasting studies. Lately, Deep Learning (DL) models started appearing within the field, with results that significantly outperform traditional ML counterparts. Even though there is a growing interest in developing models for financial Time Series Forecasting research, there is a lack of review papers that were solely focused on DL for finance. Hence, our motivation in this paper is to provide a comprehensive literature review on DL studies for financial Time Series Forecasting implementations. We not only categorized the studies according to their intended Forecasting implementation areas, such as index, forex, commodity Forecasting, but also grouped them based on their DL model choices, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Long-Short Term Memory (LSTM). We also tried to envision the future for the field by highlighting the possible setbacks and opportunities, so the interested researchers can benefit.
Francis E. H. Tay - One of the best experts on this subject based on the ideXlab platform.
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support vector machine with adaptive parameters in financial Time Series Forecasting
IEEE Transactions on Neural Networks, 2003Co-Authors: L J Cao, Francis E. H. TayAbstract:A novel type of learning machine called support vector machine (SVM) has been receiving increasing interest in areas ranging from its original application in pattern recognition to other applications such as regression estimation due to its remarkable generalization performance. This paper deals with the application of SVM in financial Time Series Forecasting. The feasibility of applying SVM in financial Forecasting is first examined by comparing it with the multilayer back-propagation (BP) neural network and the regularized radial basis function (RBF) neural network. The variability in performance of SVM with respect to the free parameters is investigated experimentally. Adaptive parameters are then proposed by incorporating the nonstationarity of financial Time Series into SVM. Five real futures contracts collated from the Chicago Mercantile Market are used as the data sets. The simulation shows that among the three methods, SVM outperforms the BP neural network in financial Forecasting, and there are comparable generalization performance between SVM and the regularized RBF neural network. Furthermore, the free parameters of SVM have a great effect on the generalization performance. SVM with adaptive parameters can both achieve higher generalization performance and use fewer support vectors than the standard SVM in financial Forecasting.
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application of support vector machines in financial Time Series Forecasting
Omega-international Journal of Management Science, 2001Co-Authors: Francis E. H. Tay, Lijuan CaoAbstract:This paper deals with the application of a novel neural network technique, support vector machine (SVM), in financial Time Series Forecasting. The objective of this paper is to examine the feasibility of SVM in financial Time Series Forecasting by comparing it with a multi-layer back-propagation (BP) neural network. Five real futures contracts that are collated from the Chicago Mercantile Market are used as the data sets. The experiment shows that SVM outperforms the BP neural network based on the criteria of normalized mean square error (NMSE), mean absolute error (MAE), directional symmetry (DS) and weighted directional symmetry (WDS). Since there is no structured way to choose the free parameters of SVMs, the variability in performance with respect to the free parameters is investigated in this study. Analysis of the experimental results proved that it is advantageous to apply SVMs to forecast financial Time Series.
Waddah Waheeb - One of the best experts on this subject based on the ideXlab platform.
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ridge polynomial neural network with error feedback for Time Series Forecasting
PLOS ONE, 2016Co-Authors: Waddah Waheeb, Rozaida Ghazali, Tutut HerawanAbstract:Time Series Forecasting has gained much attention due to its many practical applications. Higher-order neural network with recurrent feedback is a powerful technique that has been used successfully for Time Series Forecasting. It maintains fast learning and the ability to learn the dynamics of the Time Series over Time. Network output feedback is the most common recurrent feedback for many recurrent neural network models. However, not much attention has been paid to the use of network error feedback instead of network output feedback. In this study, we propose a novel model, called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that incorporates higher order terms, recurrence and error feedback. To evaluate the performance of RPNN-EF, we used four univariate Time Series with different Forecasting horizons, namely star brightness, monthly smoothed sunspot numbers, daily Euro/Dollar exchange rate, and Mackey-Glass Time-delay differential equation. We compared the Forecasting performance of RPNN-EF with the ordinary Ridge Polynomial Neural Network (RPNN) and the Dynamic Ridge Polynomial Neural Network (DRPNN). Simulation results showed an average 23.34% improvement in Root Mean Square Error (RMSE) with respect to RPNN and an average 10.74% improvement with respect to DRPNN. That means that using network errors during training helps enhance the overall Forecasting performance for the network.
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Time Series Forecasting using ridge polynomial neural network with error feedback
Soft Computing, 2016Co-Authors: Waddah Waheeb, Rozaida Ghazali, Tutut HerawanAbstract:Time Series Forecasting gets much attention due to its impact on many practical applications. Higher-order neural network with recurrent feedback is a powerful technique which used successfully for Forecasting. It maintains fast learning and the ability to learn the dynamics of the Series over Time. In general, the most used recurrent feedback is the network output. However, no much attention has been paid to use network error instead of the network output. For that, in this paper, we propose a novel model which is called Ridge Polynomial Neural Network with Error Feedback (RPNN-EF) that combines the properties of higher order and error feedback recurrent neural network. Three signals have been used in this paper, namely heat wave temperature, IBM common stock closing price and Mackey–Glass equation. Simulation results show that RPNN-EF is significantly faster than other RPNN-based models for one-step ahead Forecasting and its Forecasting performance is more significant than these models for multi-step ahead Forecasting.
