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Adaptive Forecasting of Non-Stationary Nonlinear Time Series Based on the Evolving Weighted Neuro-Neo-Fuzzy-ANARX-Model
An evolving weighted neuro-neo-fuzzy-ANARX model and its learning proced...
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Optimizing Convergence for Iterative Learning of ARIMA for Stationary Time Series
Forecasting of time series in continuous systems becomes an increasingly...
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Rank Position Forecasting in Car Racing
Forecasting is challenging since uncertainty resulted from exogenous fac...
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Deep Neural Networks and Neuro-Fuzzy Networks for Intellectual Analysis of Economic Systems
In tis paper we consider approaches for time series forecasting based on...
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Parsimonious Network based on Fuzzy Inference System (PANFIS) for Time Series Feature Prediction of Low Speed Slew Bearing Prognosis
In recent years, the utilization of rotating parts, e.g. bearings and ge...
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Dynamic Time Scan Forecasting
The dynamic time scan forecasting method relies on the premise that the ...
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High dimensional regression for regenerative time-series: an application to road traffic modeling
This paper investigates statistical models for road traffic modeling. Th...
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Inaccuracy Minimization by Partioning Fuzzy Data Sets - Validation of Analystical Methodology
In the last two decades, a number of methods have been proposed for forecasting based on fuzzy time series. Most of the fuzzy time series methods are presented for forecasting of car road accidents. However, the forecasting accuracy rates of the existing methods are not good enough. In this paper, we compared our proposed new method of fuzzy time series forecasting with existing methods. Our method is based on means based partitioning of the historical data of car road accidents. The proposed method belongs to the kth order and time-variant methods. The proposed method can get the best forecasting accuracy rate for forecasting the car road accidents than the existing methods.
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