Time Series Forecasting via a Higher Order Neural Network trained with the Extended Kalman Filter for Smart Grid Applications

2013 ◽  
pp. 254-274 ◽  
Author(s):  
Luis J. Ricalde ◽  
Glendy A. Catzin ◽  
Alma Y. Alanis ◽  
Edgar N. Sanchez
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Smart Grids ◽  
Real Data ◽  
Higher Order ◽  
Time Series Forecasting ◽  
Wind Energy Generation ◽  
The Neural Network

This chapter presents the design of a neural network that combines higher order terms in its input layer and an Extended Kalman Filter (EKF)-based algorithm for its training. The neural network-based scheme is defined as a Higher Order Neural Network (HONN), and its applicability is illustrated by means of time series forecasting for three important variables present in smart grids: Electric Load Demand (ELD), Wind Speed (WS), and Wind Energy Generation (WEG). The proposed model is trained and tested using real data values taken from a microgrid system in the UADY School of Engineering. The length of the regression vector is determined via the Lipschitz quotients methodology.

scholarly journals Nonlinear Autoregressive Neural Network and Extended Kalman Filters for Prediction of Financial Time Series

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Ghassane Benrhmach ◽  
Khalil Namir ◽  
Abdelwahed Namir ◽  
Jamal Bouyaghroumni
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Financial Time Series ◽  
Broad Class ◽  
Financial Time ◽  
The Neural Network ◽  
Nonlinear Autoregressive ◽  
Autoregressive Neural Network

Time series analysis and prediction are major scientific challenges that find their applications in fields as diverse as finance, biology, economics, meteorology, and so on. Obtaining the method with the least prediction error is one of the difficult problems of financial market and investment analysts. State space modelling is an efficient and flexible method for statistical inference of a broad class of time series and other data. The neural network is an important tool for analyzing time series especially when it is nonlinear and nonstationary. Essential tools for the study of Box-Jenkins methodology, neural networks, and extended Kalman filter were put together. We examine the use of the nonlinear autoregressive neural network method as a prediction technique for financial time series and the application of the extended Kalman filter algorithm to improve the accuracy of the model. As application on a real example, we are analyzing the time series of the daily price of steel over a 790-day period for establishing the superiority of this method over other existing methods. The simulation results using MATLAB and R software show that the model is capable of producing a reasonable accuracy.

STATISTICAL AND MACHINE LEARNING HIGH-FREQUENCY TIME SERIES FORECASTING METHODS IN AUTOMATIC MODE

2020 ◽  
pp. 3-11
Author(s):  
Sai Van Cuong ◽  
M. V. Shcherbakov
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
High Frequency ◽  
Network Models ◽  
Time Series Forecasting ◽  
Neural Network Models ◽  
Automatic Mode ◽  
Forecasting Methods ◽  
The Neural Network

The research of the problem of automatic high-frequency time series forecasting (without expert) is devoted. The efficiency of high-frequency time series forecasting using different statistical and machine learning modelsis investigated. Theclassical statistical forecasting methods are compared with neural network models based on 1000 synthetic sets of high-frequency data. The neural network models give better prediction results, however, it takes more time to compute compared to statistical approaches.

scholarly journals A Constrained Neural Network Kalman Filter for Price Estimation in High Frequency Financial Data

1997 ◽  
Vol 08 (04) ◽  
pp. 399-415 ◽  
Author(s):  
Peter J. Bolland ◽  
Jerome T. Connor
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Financial Time Series ◽  
Financial Data ◽  
The Neural Network ◽  
Tick Data ◽  
Price Estimation ◽  
Network Weight ◽  
The Stability

In this paper we present a neural network extended Kalman filter for modeling noisy financial time series. The neural network is employed to estimate the nonlinear dynamics of the extended Kalman filter. Conditions for the neural network weight matrix are provided to guarantee the stability of the filter. The extended Kalman filter presented is designed to filter three types of noise commonly observed in financial data: process noise, measurement noise, and arrival noise. The erratic arrival of data (arrival noise) results in the neural network predictions being iterated into the future. Constraining the neural network to have a fixed point at the origin produces better iterated predictions and more stable results. The performance of constrained and unconstrained neural networks within the extended Kalman filter is demonstrated on "Quote" tick data from the $/DM exchange rate (1993–1995).

scholarly journals Using an extended Kalman filter learning algorithm for feed-forward neural networks to describe tracer correlations

2004 ◽  
Vol 4 (3) ◽  
pp. 3653-3667 ◽  
Author(s):  
D. J. Lary ◽  
H. Y. Mussa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Learning Algorithm ◽  
Feed Forward ◽  
The Neural Network ◽  
Fortran Code ◽  
Feed Forward Neural Networks ◽  
Time Of Year

Abstract. In this study a new extended Kalman filter (EKF) learning algorithm for feed-forward neural networks (FFN) is used. With the EKF approach, the training of the FFN can be seen as state estimation for a non-linear stationary process. The EKF method gives excellent convergence performances provided that there is enough computer core memory and that the machine precision is high. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). The neural network was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9997. The neural network Fortran code used is available for download.

scholarly journals One-step ahead prediction of <i>fo</i>F2 using time series forecasting techniques

2005 ◽  
Vol 23 (9) ◽  
pp. 3035-3042 ◽  
Author(s):  
K. Koutroumbas ◽  
A. Belehaki
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Nearest Neighbor ◽  
Middle Latitude ◽  
Performance Criterion ◽  
Time Series Forecasting ◽  
K Nearest Neighbor ◽  
Linear Predictor ◽  
The Neural Network ◽  
One Step

Abstract. In this paper the problem of one-step ahead prediction of the critical frequency (foF2) of the middle-latitude ionosphere, using time series forecasting methods, is considered. The whole study is based on a sample of about 58000 observations of foF2 with 15-min time resolution, derived from the Athens digisonde ionograms taken from the Digisonde Portable Sounder (DPS4) located at Palaia Penteli (38° N, 23.5° E), for the period from October 2002 to May 2004. First, the embedding dimension of the dynamical system that generates the above sample is estimated using the false nearest neighbor method. This information is then utilized for the training of the predictors employed in this study, which are the linear predictor, the neural network predictor, the persistence predictor and the k-nearest neighbor predictor. The results obtained by the above predictors suggest that, as far as the mean square error is considered as performance criterion, the first two predictors are significantly better than the latter two predictors. In addition, the results obtained by the linear and the neural network predictors are not significantly different from each other. This may be taken as an indication that a linear model suffices for one step ahead prediction of foF2.

A Combined ARIMA and Neural Network Approach for Time Series Forecasting

2011 ◽  
pp. 213-225 ◽  
Author(s):  
G. Peter Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Neural Network Model ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Network Approach ◽  
Neural Network Approach ◽  
Individual Model ◽  
The Neural Network ◽  
Forecasting Performance

This chapter presents a combined ARIMA and neural network approach for time series forecasting. The model contains three steps: (1) fitting a linear ARIMA model to the time series under study, (2) building a neural network model based on the residuals from the ARIMA model, and (3) combine the ARIMA prediction and the neural network result to form the final forecast. By combining different models, we aim to take advantage of the unique modeling capability of each individual model and improve forecasting performance dramatically. The effectiveness of the combining approach is demonstrated and discussed with three applications.

Recurrent Higher Order Neural Observers for Anaerobic Processes

2010 ◽  
pp. 333-365 ◽  
Author(s):  
Edgar N. Sanchez ◽  
Diana V. Urrego ◽  
Alma Y. Alanis ◽  
Salvador Carlos-Hernandez
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Prior Knowledge ◽  
Extended Kalman Filter ◽  
Discrete Time ◽  
Higher Order ◽  
Stirred Tank ◽  
Anaerobic Process ◽  
Tank Reactor ◽  
Completely Stirred Tank Reactor

In this chapter we propose the design of a discrete-time neural observer which requires no prior knowledge of the model of an anaerobic process, for estimate biomass, substrate and inorganic carbon which are variables difficult to measure and very important for anaerobic process control in a completely stirred tank reactor (CSTR) with biomass filter; this observer is based on a recurrent higher order neural network, trained with an extended Kalman filter based algorithm.

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