Forecasting stock price based on fuzzy time-series with equal-frequency partitioning and fast Fourier transform algorithm

Chen, Bo-Tsuen; Chen, Mu-Yen; Fan, Min-Hsuan; Chen, Chia-Chen

doi:10.1109/comcomap.2012.6154806

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…Nasseri [33] presented a fusion method employing the extended Kalman filter and genetic algorithm for predicting water demand. In turn, Bo-Tsuen Chen, et al [41] transferred the Fourier transform framework into a fuzzy time series prediction scheme for stock prices. Sang [42] suggested a modified wavelet transform-based denoising framework.…”

Section: Artificial Intelligence Struggles With Forecasting Issuesmentioning

confidence: 99%

Hybrid Convolutional Neural Networks Based Framework for Skimmed Milk Powder Price Forecasting

Malczewski

Czubak

2021

Sustainability

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The latest studies have compellingly argued that Neural Networks (NN) classification and prediction are the right direction for forecasting. It has been proven that NN are suitable models for any continuous function. Moreover, these methods are superior to conventional methods, such a Box–Jenkins, AR, MA, ARMA, or ARIMA. The latter assume a linear relationship between inputs and outputs. This assumption is not valid for skimmed milk powder (SMP) forecasting, because of nonlinearities, which are supposed to be approximated. The traditional prediction methods need complete date. The non-AI-based techniques regularly handle univariate-like data only. This assumption is not sufficient, because many external factors might influence the time series. It should be noted that any Artificial Neural Network (ANN) approach can be strongly affected by the relevancy and “clarity” of its input training data. In the proposed Convolutional Neural Networks based methodology assumes price series data to be sparse and noisy. The presented procedure utilizes Compressed Sensing (CS) methodology, which assumes noisy trends are incomplete signals for them to be reconstructed using CS reconstruction algorithms. Denoised trends are more relevant in terms of NN-based forecasting models’ prediction performance. Empirical results reveal robustness of the proposed technique.

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Section: Artificial Intelligence Struggles With Forecasting Issuesmentioning

confidence: 99%

Hybrid Convolutional Neural Networks Based Framework for Skimmed Milk Powder Price Forecasting

Malczewski

Czubak

2021

Sustainability

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“…Nasseri et al (2011) proposed a hybrid model coupling the extended Kalman filter and genetic programming for forecasting water demand. Chen et al (2012) introduced Fourier transform into a fuzzy time series forecasting model for stock price. He et al (2012) proposed a novel multivariate wavelet denoising based approach for estimating portfolio value at risk (PVaR).…”

Section: Introductionmentioning

confidence: 99%

A compressed sensing based AI learning paradigm for crude oil price forecasting

2014

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“…The author of [20] suggested a unique multivariate wavelet denoising-based method for assessing the portfolio value at risk (PVaR). The author of [21] suggested an enhanced wavelet modeling framework for eliminating noise in time-series forecasting.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Accuracy of Traditional Regression, Machine Learning, and Deep Learning: A Study of Environmental Emissions in Saudi Arabia

Sarwar,

Aziz,

Balsalobre-Lorente

2023

Sustainability

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Currently, the world is facing the problem of climate change and other environmental issues due to higher emissions of greenhouse gases. Saudi Arabia is not an exception due to the dependence of the Saudi economy on fossil fuels, which adds to the problem. However, due to the nonlinear pattern of pollution-creating gases, including nitrogen and sulfur dioxide, it is not effortless to rely on forecasting accuracy. Nevertheless, it is essential to denoise the data to extract the reliable outcomes used by different econometric approaches. Hence, the current paper introduces a hybrid model combining compressed sensor denoising (CSD) with traditional regression, machine learning, and deep learning techniques. Comparing different hybrid models and various denoising techniques revealed that CSD-GAN is the best model for accurately predicting NO2 and SO2, as compared with ARIMA, RLS, and SVR. Also, when the comparison is made between predicted and actual NO2 and SO2 levels, these are aligned, proving that CSD-GAN is superior in its level and direction of prediction. It can be concluded that the GAN model is the best hybrid model for predicting NO2 and SO2 emissions in Saudi Arabia. Hence, this model is recommended to policymakers for predicting environmental externalities and framing policies accordingly.

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Forecasting stock price based on fuzzy time-series with equal-frequency partitioning and fast Fourier transform algorithm

Cited by 8 publications

References 27 publications

Hybrid Convolutional Neural Networks Based Framework for Skimmed Milk Powder Price Forecasting

Hybrid Convolutional Neural Networks Based Framework for Skimmed Milk Powder Price Forecasting

A compressed sensing based AI learning paradigm for crude oil price forecasting

Forecasting Accuracy of Traditional Regression, Machine Learning, and Deep Learning: A Study of Environmental Emissions in Saudi Arabia

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