Short-term financial forecasting using ANN adaptive predictors in cascade

Dobrescu, Emilian; Nastac, Dumitru Iulian; Pelinescu, Elena

doi:10.1504/ijpmb.2014.065519

Cited by 8 publications

(3 citation statements)

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“…It is based on the use of serially connected RBF-SVRs. This non-linear artificial intelligence tool ensures high work speed, and its use in a cascade structure increases prediction accuracy 22 . The composition of the ensemble in this case involves the serial connection of all SVRs with RBF-input extension, where the output of the previous member of the ensemble is taken into account only at the next node.…”

Section: State-of-the-artsmentioning

confidence: 99%

An interpretable ensemble structure with a non-iterative training algorithm to improve the predictive accuracy of healthcare data analysis

Izonin,

Tkachenko,

Yemets

et al. 2024

Sci Rep

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The modern development of healthcare is characterized by a set of large volumes of tabular data for monitoring and diagnosing the patient's condition. In addition, modern methods of data engineering allow the synthesizing of a large number of features from an image or signals, which are presented in tabular form. The possibility of high-precision and high-speed processing of such large volumes of medical data requires the use of artificial intelligence tools. A linear machine learning model cannot accurately analyze such data, and traditional bagging, boosting, or stacking ensembles typically require significant computing power and time to implement. In this paper, the authors proposed a method for the analysis of large sets of medical data, based on a designed linear ensemble method with a non-iterative learning algorithm. The basic node of the new ensemble is an extended-input SGTM neural-like structure, which provides high-speed data processing at each level of the ensemble. Increasing prediction accuracy is ensured by dividing the large dataset into parts, the analysis of which is carried out in each node of the ensemble structure and taking into account the output signal from the previous level of the ensemble as an additional attribute on the next one. Such a design of a new ensemble structure provides both a significant increase in the prediction accuracy for large sets of medical data analysis and a significant reduction in the duration of the training procedure. Experimental studies on a large medical dataset, as well as a comparison with existing machine learning methods, confirmed the high efficiency of using the developed ensemble structure when solving the prediction task.

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Section: State-of-the-artsmentioning

confidence: 99%

An interpretable ensemble structure with a non-iterative training algorithm to improve the predictive accuracy of healthcare data analysis

Izonin,

Tkachenko,

Yemets

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Recently, the Artificial Neural Network (ANN) model has been widely used for time series forecasting, for example, in agriculture [11], [12], finance and economic [13]- [15], energy [16]- [18], weather [19]- [21], transportation [22], and others. The ANN model can forecast something in the future based on historical patterns in the past [23].…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Currency Circulation in Indonesia Using Hybrid Extreme Learning Machine

Kartikasari

2022

BAREKENG: J. Il. Mat. & Ter.

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Forecasting currency circulation, including inflow and outflow, is one of Bank Indonesia's strategies to maintain the Rupiah value's stability. The characteristic of inflow and outflow data is that they have seasonal variations. This study proposes a hybrid model by combining decomposition techniques and Extreme Learning Machine to overcome data that has seasonal variations. The forecasting results of the proposed model are compared with the original Extreme Learning Machine. The comparison results show that the forecasting results with the hybrid model have the smallest errors. Thus, the hybrid model can predict data with seasonal variations better than the original Extreme Learning Machine.

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“…(1) Electricity load forecasting [21,22] (2) Financial forecasting [10,23] (3) Monthly average rainfall prediction [24] is study is summarized in five sections. e first and current section contains the brief introduction of problem statement and various solutions given by the scholars.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Forecasting of Agriculture Commodities in Context of Indian Market for Sustainable Agriculture by Using the Artificial Neural Network

Mahto

Alam

Biswas

et al. 2021

Journal of Food Quality

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Prediction of well-grounded market information, particularly short-term forecast of prices of agricultural commodities, is the essential requirement for the sustainable development of the farming community. Such predictions are mostly performed with the help of time series models. In this study, the soft computing method is used for short-term forecasting of agriculture commodity price based on time series data using the artificial neural network (ANN). The time series data for sunflower seed and soybean seed are considered as the agriculture commodities. The soybean seed time series data were collected for the period of five years (Jan 2014–Dec 2018), for Akola district market, Maharashtra, India. The sunflower time series data were collected for the period of six years (Jan 2011–Dec 2016), for Kadari district market, Andhra Pradesh, India. The dataset is available at the Indian government website taken from the website www.data.gov.in. For forecasting, the ANN model is used on the abovementioned datasets. The performance of the model is compared with the result of the traditional ARIMA model. The mean absolute percentage error (MAPE) and root mean square percentage error (RMSPE) are considered as the performance parameters for the forecasting model. It is observed that the ANN is a better forecasting model than the ARIMA model by considering the two forecasting performance parameters MAPE and RMSPE.

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Short-term financial forecasting using ANN adaptive predictors in cascade

Cited by 8 publications

References 0 publications

An interpretable ensemble structure with a non-iterative training algorithm to improve the predictive accuracy of healthcare data analysis

An interpretable ensemble structure with a non-iterative training algorithm to improve the predictive accuracy of healthcare data analysis

Forecasting of Currency Circulation in Indonesia Using Hybrid Extreme Learning Machine

Short-Term Forecasting of Agriculture Commodities in Context of Indian Market for Sustainable Agriculture by Using the Artificial Neural Network

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