Forecasting performance of wavelet neural networks and other neural network topologies: A comparative study based on financial market data sets

Vogl, Markus; Rötzel, Peter; Homes, Stefan

doi:10.1016/j.mlwa.2022.100302

Cited by 14 publications

(6 citation statements)

References 81 publications

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“…The authors applied DWT for data denoising. Vogl et al (2022) investigated the advantageous effects of the combination of wavelet and neural networks. The authors indicate that the wavelet can be an effective tool in the decomposition of the time series and caused to improve the accuracy of forecasting models.…”

Section: Literature Review Of Past Workmentioning

confidence: 99%

Futuristic portfolio optimization problem: wavelet based long short-term memory

Abolmakarem,

Abdi,

Khalili-Damghani

et al. 2023

JM2

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Purpose This paper aims to propose an improved version of portfolio optimization model through the prediction of the future behavior of stock returns using a combined wavelet-based long short-term memory (LSTM). Design/methodology/approach First, data are gathered and divided into two parts, namely, “past data” and “real data.” In the second stage, the wavelet transform is proposed to decompose the stock closing price time series into a set of coefficients. The derived coefficients are taken as an input to the LSTM model to predict the stock closing price time series and the “future data” is created. In the third stage, the mean-variance portfolio optimization problem (MVPOP) has iteratively been run using the “past,” “future” and “real” data sets. The epsilon-constraint method is adapted to generate the Pareto front for all three runes of MVPOP. Findings The real daily stock closing price time series of six stocks from the FTSE 100 between January 1, 2000, and December 30, 2020, is used to check the applicability and efficacy of the proposed approach. The comparisons of “future,” “past” and “real” Pareto fronts showed that the “future” Pareto front is closer to the “real” Pareto front. This demonstrates the efficacy and applicability of proposed approach. Originality/value Most of the classic Markowitz-based portfolio optimization models used past information to estimate the associated parameters of the stocks. This study revealed that the prediction of the future behavior of stock returns using a combined wavelet-based LSTM improved the performance of the portfolio.

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Section: Literature Review Of Past Workmentioning

confidence: 99%

Futuristic portfolio optimization problem: wavelet based long short-term memory

Abolmakarem,

Abdi,

Khalili-Damghani

et al. 2023

JM2

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“…This is crucial for analyzing nonlinear and non-stationary economic and financial time series, which can interact differently on different time scales [26][27][28][29][30][31][32][33][34][35]. In connection with such undoubted advantages, methods for forecasting nonlinear non-stationary economic and financial time series based on wavelet packet transform and combined methods have recently been actively developed, including Wavelet Artificial Neural Networks (WANN), Wavelet Least-Squares Support Vector Machine (WLSSVM), and Multivariate Adaptive Regression Splines (MARS) [36][37][38][39][40][41][42][43][44][45][46]. Their results indicate a significant increase in the performance and accuracy of traditional time series forecasting models in combination with wavelet packet transform (WPT).…”

Section: Literature Reviewmentioning

confidence: 99%

Improving the Accuracy of Forecasting the TSA Daily Budgetary Fund Balance Based on Wavelet Packet Transforms

Karaev

Gorlova

Sedova

et al. 2022

Journal of Open Innovation: Technology, Market, and Complexity

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Improving the accuracy of cash flow forecasting in the TSA is the key to fulfilling government payment obligations, minimizing the cost of maintaining the cash reserve, providing the absence of outstanding debt accumulation, and ensuring investment in various financial instruments to obtain additional income. The article describes a method for improving the accuracy of forecasting a time series composed of daily budgetary fund balances in the TSA, based on its preliminary decomposition using a discrete wavelet packet transform of the Daubechies family. This makes it possible to increase the accuracy of traditional forecasting methods from 80% to more than 96%. The decomposition level varied from one to eight to minimize the mean absolute error and improve the forecasting accuracy. Calculations of statistical tests for adequacy confirm the effectiveness of the proposed method for improving forecasting accuracy. The scientific novelty of the proposed method for improving the forecasting accuracy of time series from daily budgetary fund balances in the TSA lies in proving the need for preliminary timeseries decomposition and subsequent construction of forecasts for the obtained parts, resulting in high forecasting accuracy. The result differs significantly from traditional econometric methods (ARIMA/SARIMA), characterized by a much lower accuracy (50–80%) and a decrease in forecasting accuracy with an increase in the forecast horizon. This article is novel, as it forms a new approach to solving the problem of increasing the efficiency of using budgetary funds, associated with improving the accuracy of forecasting daily budgetary fund balance in the TSA.

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“…Many specialists and scholars utilize stochastic numerical methods mainly due to their utility and worth. The corneal shaped model [40], COVID19 system model [41], dusty plasma model [42], financial market forecasting [43], and mosquito dispersal [44] are instances of models that use stochastic numerical techniques. Many fluid flow systems have lately been developed with intelligent computing paradigms [45][46][47][48][49], highlighting the utility of these new stochastic solvers.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Bayesian regularization‐based solution predictive procedure for hybrid nanoparticles of AA7072‐AA7075 oxide movement across a porous medium

et al. 2023

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The research community has shown great interest for investigation in the nanofluids models involving Aluminum Alloys AA7072 and AA7072 +AA7075 due to their advantageous impact on heat transfer, physical and mechanical characteristic exploiting in broad engineering applications such as manufacturing of spacecraft, aircraft parts and building testing. The hybrid nanomaterial AA7072-AA7075 based fluidic system is investigated in this paper using an artificial neural network with Bayesian regularization scheme (ANNs-BRS). The derived partial differential equations (PDEs) are transformed into ordinary differential equations system ODEs and obtained the reference datasets for the estimated solution dynamics of hybrid nanofluidic system. For prominent parameters, the influence of flow on the temperature distribution and velocity plot are investigated. The performance on 80% training samples, 5% testing and 15% validation dataset for ANNs-BRS is well-established in terms of error histogram plots, regression analysis, and MSE based statistics. The results for entropy generation, Eckert number Ec, magnetic interaction parameter M, suction parameter S, and heat generation parameter 𝑄 are also discussed. The results show that the Eckert number Ec has the effect of slowing down the rate of heat transfer while increasing the temperature and increases in suction parameter causes decrease in temperature while increase in temperature profile due to enhancement of suction parameter.

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Forecasting performance of wavelet neural networks and other neural network topologies: A comparative study based on financial market data sets

Cited by 14 publications

References 81 publications

Futuristic portfolio optimization problem: wavelet based long short-term memory

Futuristic portfolio optimization problem: wavelet based long short-term memory

Improving the Accuracy of Forecasting the TSA Daily Budgetary Fund Balance Based on Wavelet Packet Transforms

Intelligent Bayesian regularization‐based solution predictive procedure for hybrid nanoparticles of AA7072‐AA7075 oxide movement across a porous medium

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