scholarly journals Chaotic Time Series Forecasting Using Higher Order Neural Networks

2016 ◽  
Vol 6 (5) ◽  
pp. 624 ◽  
Author(s):  
Waddah Waheeb ◽  
Rozaida Ghazali
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Higher Order ◽  
Time Series Forecasting ◽  
Chaotic Time Series ◽  
Higher Order Neural Networks

Electric Load Demand and Electricity Prices ForecastingUsing Higher Order Neural Networks Trained by Kalman Filtering

2009 ◽  
pp. 295-313 ◽  
Author(s):  
Edgar N. Sanchez ◽  
Alma Y. Alanis ◽  
Jesús Rico
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Kalman Filtering ◽  
Chaotic Behavior ◽  
Higher Order ◽  
Electric Load ◽  
Electricity Prices ◽  
Load Demand ◽  
Higher Order Neural Networks

In this chapter, we propose the use of Higher Order Neural Networks (HONNs) trained with an extended Kalman filter based algorithm to predict the electric load demand as well as the electricity prices, with beyond a horizon of 24 hours. Due to the chaotic behavior of the electrical markets, it is not advisable to apply the traditional forecasting techniques used for time series; the results presented here confirm that HONNs can very well capture the complexity underlying electric load demand and electricity prices. The proposed neural network model produces very accurate next day predictions and also, prognosticates with very good accuracy, a week-ahead demand and price forecasts.

Ultra High Frequency Trigonometric Higher Order Neural Networks for Time Series Data Analysis

2009 ◽  
pp. 133-163 ◽  
Author(s):  
Ming Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Data Analysis ◽  
Exchange Rates ◽  
Time Series Data ◽  
Higher Order ◽  
Series Data ◽  
Higher Order Neural Networks ◽  
Time Series Data Analysis

This chapter develops a new nonlinear model, Ultra high frequency Trigonometric Higher Order Neural Networks (UTHONN), for time series data analysis. Results show that UTHONN models are 3 to 12% better than Equilibrium Real Exchange Rates (ERER) model, and 4 – 9% better than other Polynomial Higher Order Neural Network (PHONN) and Trigonometric Higher Order Neural Network (THONN) models. This study also uses UTHONN models to simulate foreign exchange rates and consumer price index with error approaching 0.0000%.

scholarly journals Application of Higher-Order Neural Networks to Financial Time-Series Prediction

2006 ◽  
pp. 80-108 ◽  
Author(s):  
John Fulcher ◽  
Ming Zhang ◽  
Shuxiang Xu
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Data ◽  
Financial Time Series ◽  
Time Series Prediction ◽  
Higher Order ◽  
Financial Time ◽  
Polynomial Coefficients ◽  
Higher Order Neural Networks ◽  
Piecewise Continuous

Financial time-series data is characterized by nonlinearities, discontinuities, and high-frequency multipolynomial components. Not surprisingly, conventional artificial neural networks (ANNs) have difficulty in modeling such complex data. A more appropriate approach is to apply higher-order ANNs, which are capable of extracting higher-order polynomial coefficients in the data. Moreover, since there is a one-to-one correspondence between network weights and polynomial coefficients, higher-order neural networks (HONNs) — unlike ANNs generally — can be considered open-, rather than “closed-box” solutions, and thus hold more appeal to the financial community. After developing polynomial and trigonometric HONNs (P[T]HONNs), we introduce the concept of HONN groups. The latter incorporate piecewise continuous-activation functions and thresholds, and as a result are capable of modeling discontinuous (or piecewise-continuous) data, and what is more to any degree of accuracy. Several other PHONN variants are also described. The performance of P(T)HONN and HONN groups on representative financial time series is described (i.e., credit ratings and exchange rates). In short, HONNs offer roughly twice the performance of MLP/BP on financial time-series prediction, and HONN groups around 10% further improvement.

scholarly journals A Modified Weight Optimization for Artificial Higher Order Neural Networks in Physical Time Series

2020 ◽  
Vol 11 (3) ◽  
Author(s):  
Noor Aida Husaini ◽  
Rozaida Ghazali ◽  
Nureize Arbaiy ◽  
Norhamreeza Abdul ◽  
Lokman Hakim
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Higher Order ◽  
Weight Optimization ◽  
Physical Time ◽  
Higher Order Neural Networks
Sign in / Sign up

Export Citation Format

Share Document