Chebyshev polynomial functions based locally recurrent neuro-fuzzy information system for prediction of financial and energy market data

Parida, A.K.; Bisoi, Ranjeeta; Dash, P. K.

doi:10.1016/j.jfds.2016.10.001

Cited by 10 publications

(5 citation statements)

References 50 publications

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“…[123,124,125,126,127,128,129,130,131,132,133,134,114] used S&P500 as their dataset. The authors of [123,124,135,136,137] used NIKKEI as their dataset. KOSPI was used in [135,131,132].…”

Section: Index Forecastingmentioning

confidence: 99%

“…The authors of [148] proposed a (Threshold Autoregressive (TAR)-Vector Error Correction model (VEC)-Recurrent Hybrid Elman (RHE)) model for forex and stock index of return prediction and compared several models. The authors of [124] proposed a method that is called Locally Recurrent Neuro-fuzzy Information System (LRNFIS) with Firefly Harmony Search Optimization (FHSO) Evolutionary Algorithm (EA) to predict S&P500, NIKKEI225 indices and USD Exchange price data. The authors of [149] proposed a Heterogeneous Autoregressive Process (HAR) with a GA with a SVR (GASVR) model that was called HAR-GASVR for prediction of VIX, VXN, Dow Jones Industrial Average Volatility Index (VXD) indices.…”

Section: Index Forecastingmentioning

confidence: 99%

“…Different event-types on Chinese companies were classified based on a novel event-type pattern classification algorithm in [122], also next day stock price change was predicted using additional inputs. [123,124,125,126,127,128,129,130,131,132,133,134,114] used S&P500 as their dataset. The authors of [123,124,135,136,137] used NIKKEI as their dataset.…”

Section: Stock Price Forecastingmentioning

confidence: 99%

“…In [164], the authors compared several models including traditional forecasting models and DL models for DEM/GBP prediction. The authors in [124] predicted AUD, CHF, MAX and BRL against USD currency time series data using LRNFIS and compared it with different models. Meanwhile, instead of using LMS based error minimization during the learning, they used FHSO.…”

Section: Forex Price Forecastingmentioning

confidence: 99%

See 3 more Smart Citations

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

720

294

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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.

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“…[123,124,125,126,127,128,129,130,131,132,133,134,114] used S&P500 as their dataset. The authors of [123,124,135,136,137] used NIKKEI as their dataset. KOSPI was used in [135,131,132].…”

Section: Index Forecastingmentioning

confidence: 99%

Section: Index Forecastingmentioning

confidence: 99%

Section: Stock Price Forecastingmentioning

confidence: 99%

Section: Forex Price Forecastingmentioning

confidence: 99%

See 2 more Smart Citations

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Sezer

Gudelek

Özbayoğlu

2020

Applied Soft Computing

720

294

View full text Add to dashboard Cite

show abstract

“…Chebyshev polynomial function provides an extended nonlinear transformation for the input space, thus increasing its dimension to capture nonlinear and chaotic changes in financial or energy market data streams. The local recurrent neuro fuzzy information system (lrnfis) includes feedback loops in both the emission intensity layer and the output layer to allow the signal to flow forward and backward, so that the lrnfis can simulate a dynamic system and provide fast convergence and accuracy in predicting time series fluctuations [7].…”

Section: B Construction Of Rural Financial Information Service Platformmentioning

confidence: 99%

Rural Financial Information Service Platform Under Smart Financial Environment

Dai

2020

IEEE Access

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With the continuous deepening of economic and social development and reform, the problems in rural financial development have gradually exposed. Therefore, in order to speed up the rural economic development and promote the construction of new rural areas. It is very necessary and urgent to promote the innovation of rural financial services and create a modern rural financial service platform. In order to solve the problems of information collection, processing and integration of rural financial information service platform in China, the diversification, personalization, timeliness and accuracy of information demand are difficult to be met, and the organization and operation mode of the platform are not perfect. In this paper, based on the intelligent sensor, the whole digital transformation is realized through the reference of big data. Based on this, this paper establishes the research model of rural financial information service platform under the smart financial environment. From the current situation of the construction and application of rural financial information service platform in China, it studies the basic situation of the construction of rural financial information service platform in China from three aspects of functional scope, service mode and operation mode, and draws the improvement conclusion. The results show that the efficiency of rural financial information service is increased by 20% after using the improved method in this paper, which has certain use value.

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Lagging problem in financial time series forecasting

Song

et al. 2023

Neural Comput & Applic

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Chebyshev polynomial functions based locally recurrent neuro-fuzzy information system for prediction of financial and energy market data

Cited by 10 publications

References 50 publications

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Financial time series forecasting with deep learning : A systematic literature review: 2005–2019

Rural Financial Information Service Platform Under Smart Financial Environment

Lagging problem in financial time series forecasting

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