scholarly journals Hybrid Model for the Analysis of Human Gait: A Non-linear Approach

2020 ◽  
pp. 216-231
Author(s):  
Ramón E. R. González ◽  
Carlos Collazos-Morales ◽  
João P. Galdino ◽  
P. H. Figueiredo ◽  
Juan Lombana ◽  
...  
Keyword(s):  
Hybrid Model ◽  
Human Gait ◽  
Linear Approach ◽  
Non Linear

Overcoming “The Third Way” Concept

2006 ◽  
pp. 126-134
Author(s):  
L. Evstigneeva ◽  
R. Evstigneev
Keyword(s):  
Economic Research ◽  
Economic Uncertainty ◽  
Third Way ◽  
The Third ◽  
Linear Approach ◽  
The World ◽  
Non Linear

“The Third Way” concept is still widespread all over the world. Growing socio-economic uncertainty makes the authors revise the concept. In the course of discussion with other authors they introduce a synergetic vision of the problem. That means in the first place changing a linear approach to the economic research for a non-linear one.

Energy resources demand-supply system analysis and empirical research based on non-linear approach

Energy ◽  
2011 ◽  
Vol 36 (9) ◽  
pp. 5460-5465 ◽  
Author(s):  
Mei Sun ◽  
Xiaofang Wang ◽  
Ying Chen ◽  
Lixin Tian
Keyword(s):  
Empirical Research ◽  
System Analysis ◽  
Supply System ◽  
Energy Resources ◽  
Linear Approach ◽  
Non Linear

scholarly journals Forecasting stock price movement: new evidence from a novel hybrid deep learning model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yang Zhao ◽  
Zhonglu Chen
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Hybrid Model ◽  
Stock Price ◽  
Linear Time ◽  
Price Movement ◽  
Content Type ◽  
Non Linear ◽  
New Evidence ◽  
Deep Learning Model

PurposeThis study explores whether a new machine learning method can more accurately predict the movement of stock prices.Design/methodology/approachThis study presents a novel hybrid deep learning model, Residual-CNN-Seq2Seq (RCSNet), to predict the trend of stock price movement. RCSNet integrates the autoregressive integrated moving average (ARIMA) model, convolutional neural network (CNN) and the sequence-to-sequence (Seq2Seq) long–short-term memory (LSTM) model.FindingsThe hybrid model is able to forecast both linear and non-linear time-series component of stock dataset. CNN and Seq2Seq LSTMs can be effectively combined for dynamic modeling of short- and long-term-dependent patterns in non-linear time series forecast. Experimental results show that the proposed model outperforms baseline models on S&P 500 index stock dataset from January 2000 to August 2016.Originality/valueThis study develops the RCSNet hybrid model to tackle the challenge by combining both linear and non-linear models. New evidence has been obtained in predicting the movement of stock market prices.

A consistent geometrically non-linear approach for delamination

2002 ◽  
Vol 54 (9) ◽  
pp. 1333-1355 ◽  
Author(s):  
G. N. Wells ◽  
R. de Borst ◽  
L. J. Sluys
Keyword(s):  
Linear Approach ◽  
Non Linear

scholarly journals Hybrid MARMA-NARX model for flow forecasting based on the large-scale climate signals, sea-surface temperatures, and rainfall

2018 ◽  
Vol 49 (6) ◽  
pp. 1788-1803 ◽  
Author(s):  
Mohammad Ebrahim Banihabib ◽  
Arezoo Ahmadian ◽  
Mohammad Valipour
Keyword(s):  
Linear Model ◽  
Hybrid Model ◽  
Large Scale ◽  
Linear Models ◽  
Moving Average ◽  
Effective Parameters ◽  
Forecasting Accuracy ◽  
Local Factor ◽  
Non Linear ◽  
Climate Signals

Abstract In this study, to reflect the effect of large-scale climate signals on runoff, these indices are accompanied with rainfall (the most effective local factor in runoff) as the inputs of the hybrid model. Where one-year in advance forecasting of reservoir inflows can provide data to have an optimal reservoir operation, reports show we still need more accurate models which include all effective parameters to have more forecasting accuracy than traditional linear models (ARMA and ARIMA). Thus, hybridization of models was employed for improving the accuracy of flow forecasting. Moreover, various forecasters including large-scale climate signals were tested to promote forecasting. This paper focuses on testing MARMA-NARX hybrid model to enhance the accuracy of monthly inflow forecasts. Since the inflow in different periods of the year has in linear and non-linear trends, the hybrid model is proposed as a means of combining linear model, monthly autoregressive moving average (MARMA), and non-linear model, nonlinear autoregressive model with exogenous (NARX) inputs to upgrade the accuracy of flow forecasting. The results of the study showed enhanced forecasting accuracy through using the hybrid model.

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