A fast evidential approach for stock forecasting

Zhan, Tianxiang; Xiao, Fuyuan

doi:10.1002/int.22598

Cited by 14 publications

(9 citation statements)

References 52 publications

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“…In this section, we present the performance comparison among the forecasting approaches: MVA, MXA [30], ARIMA, SVM, LSTM, MLP, Hybrid additive ARIMA-ANN (HAAA), Hybrid additive ETS-ANN (HAEA) and naive estimation. To measure the accuracy quantitatively, we calculated the error measurements defined in the equations ( 8), (9), and (10). It is important to point out that all forecasting processes are one-step-ahead type.…”

Section: Results and Analysismentioning

confidence: 99%

“…This is a dynamic research area that has attracted the attention of the scientific community over the past few decades [2]. Applications of time series modeling and analysis encompass several fields of science, including: medicine [3], robotics [4], cyber defense [5], defense strategy [6], army mission analysis [7], finance [8], [9], social sciences [10], economics [11], seismology [12] and criminology [13]. In order to make estimates of the future terms of a time series, it is necessary to make the hypothesis that each observed data is somehow correlated with past data.…”

Section: Introductionmentioning

confidence: 99%

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Maximum Visibility: A Novel Approach for Time Series Forecasting Based on Complex Network Theory

2022

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This article presents Maximum Visibility Approach (MVA), a new time series forecasting method based on the Complex Network theory. MVA initially maps time series data into a complex network using the visibility graph method. Then, based on the similarity measures between the nodes in the network, MVA calculates the one-step-ahead forecasts. MVA does not use all past terms in the forecasting process, but only the most significant observations, which are indicated as a result of the autocorrelation function. This method was applied to five different groups of data, most of them showing trend characteristics, seasonal variations and/or non-stationary behavior. We calculated error measures to evaluate the performance of MVA. The results of statistical tests and error measures revealed that MVA has a good performance compared to the accuracy obtained by the benchmarks considered in this work. In all cases, MVA surpassed other forecasting methods in Literature, which confirms that this work will contribute to the field of time series forecasting not only in the theoretical aspect, but also in practice.

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Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Maximum Visibility: A Novel Approach for Time Series Forecasting Based on Complex Network Theory

2022

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“…Due to the effectiveness of the VG algorithm, the family of VG algorithms has been applied in different fields to address practical problems, such as financial time series [ 115 – 117 ], electroencephalogram (EEG) signal [ 70 , 118 ], traffic data [ 119 , 120 ], earthquake time series [ 121 , 122 ], and market dataset [ 123 , 124 ].…”

Section: Visibility Graphmentioning

confidence: 99%

Visibility graph for time series prediction and image classification: a review

2022

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The analysis of time series and images is significant across different fields due to their widespread applications. In the past few decades, many approaches have been developed, including data-driven artificial intelligence methods, mechanism-driven physical methods, and hybrid mechanism and data-driven models. Complex networks have been used to model numerous complex systems due to its characteristics, including time series prediction and image classification. In order to map time series and images into complex networks, many visibility graph algorithms have been developed, such as horizontal visibility graph, limited penetrable visibility graph, multiplex visibility graph, and image visibility graph. The family of visibility graph algorithms will construct different types of complex networks, including (un-) weighted, (un-) directed, and (single-) multi-layered networks, thereby focusing on different kinds of properties. Different types of visibility graph algorithms will be reviewed in this paper. Through exploring the topological structure and information in the network based on statistical physics, the property of time series and images can be discovered. In order to forecast (multivariate) time series, several variations of local random walk algorithms and different information fusion approaches are applied to measure the similarity between nodes in the network. Different forecasting frameworks are also proposed to consider the information in the time series based on the similarity. In order to classify the image, several machine learning models (such as support vector machine and linear discriminant) are used to classify images based on global features, local features, and multiplex features. Through various simulations on a variety of datasets, researchers have found that the visibility graph algorithm outperformed existing algorithms, both in time series prediction and image classification. Clearly, complex networks are closely connected with time series and images by visibility graph algorithms, rendering complex networks to be an important tool for understanding the characteristics of time series and images. Finally, we conclude in the last section with future outlooks for the visibility graph.

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“…Neural networks show considerable promise for predicting noisy and chaotic time series data in a range of contexts (Xu and Zhang, 2023j, q;Karasu et al, 2017a, b), including the financial and economic sectors (Xu and Zhang, 2023d, s;Kumar et al, 2021;Xu, 2015bXu, , 2018aYang et al, 2008Yang et al, , 2010Wang and Yang, 2010;Karasu et al, 2020;Wegener et al, 2016), according to different studies. Their ability to foresee and recognize nonlinear patterns (Xu and Zhang, 2021c;Altan et al, 2021;Xu, 2018c) in a variety of time series (Xu and Zhang, 2021d, 2023aAbraham et al, 2020;Zhan and Xiao, 2021) through self-learning Zhang, 2023n, 2024d;Karasu et al, 2020) may be helpful in this respect. In this case, we employ a neural network to predict the price of green beans, a crucial agricultural commodity on the Chinese market.…”

Section: Introductionmentioning

confidence: 99%

Wholesale price forecasts of green grams using the neural network

Jin,

2024

AJEB

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PurposeAgriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.Design/methodology/approachIn order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.FindingsOur model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.Originality/valueUtilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

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A fast evidential approach for stock forecasting

Cited by 14 publications

References 52 publications

Maximum Visibility: A Novel Approach for Time Series Forecasting Based on Complex Network Theory

Maximum Visibility: A Novel Approach for Time Series Forecasting Based on Complex Network Theory

Visibility graph for time series prediction and image classification: a review

Wholesale price forecasts of green grams using the neural network

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