Recurrent neural networks and discrete wavelet transform for time series modeling and prediction

2002 ◽  
Author(s):  
Fu-Chiang Tsui ◽  
Mingui Sun ◽  
Ching-Chung Li ◽  
R.J. Sclabassi
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Recurrent Neural Networks ◽  
Discrete Wavelet ◽  
Time Series Modeling ◽  
Modeling And Prediction

scholarly journals The Shift Invariant Discrete Wavelet Transform (SIDWT) with Inflation Time Series Application

2016 ◽  
Vol 8 (4) ◽  
pp. 14
Author(s):  
Suparti Suparti ◽  
Rezzy Eko Caraka ◽  
Budi Warsito ◽  
Hasbi Yasin
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Well Being ◽  
Discrete Wavelet ◽  
Time Series Modeling ◽  
The Government ◽  
Determining Factor ◽  
Analysis Of Time Series ◽  
Economic Well Being

<p>Analysis of time series used in many areas, one of which is in the field economy. In this research using time series on inflation using Shift Invariant Discrete Wavelet Transform (SIDWT).Time series decomposition using transformation wavelet namely SIDWT with Haar filter and D4. Results of the transformation, coefficient of drag coefficient wavelet and scale that is used for modeling time series. Modeling done by using Multiscale Autoregressive (MAR). In a certain area, inflation to it is an important that he had made the standard-bearer of economic well-being of society, the factors Directors investors in selecting a kind of investment, and the determining factor for the government to formulate policy fiscal, monetary, as well as non-monetary that will be applied. Inflation can be analyzed using methods Shift Invariant Discrete Wavelet Transform (SIDWT) which had been modeled for them to use Mulitiscale Autoregressive (MAR) with the R2 value 93.62%.</p>

scholarly journals Neural Networks Multiscale Autoregressive untuk Peramalan Data Time Series

2011 ◽  
Vol 3 (01) ◽  
Author(s):  
Brodjol Sutijo
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet

Transformasi wavelet yang banyak digunakan untuk peramalan time series adalah Maximal Overlap Discrete Wavelet Transform (MODWT). Penggunaan MODWT adalah untuk mengatasi keterbatasan discrete wavelet transform (DWT), yang membutuhkan N=2j dimana J adalah bilangan bulat positif. Praktis, data time series jarang memenuhi kondisinya tersebut. Skala dan koefisien wavelet yang diberikan oleh MODWT akan digunakan untuk peramalan time series. Ada beberapa penelitian yang berkaitan dengan NN-MAR untuk peramalan time series, biasanya berfokus pada bagaimana untuk mendapatkan model NN-MAR yang sesuai untuk peramalan data time series. Tujuan dari penelitian ini adalah untuk mengembangkan prosedur yang tepat untuk pemodelan NN-MAR data time series musiman, dan untuk membandingkan akurasi ramalan antara NN-MAR, ARIMA, dan MAR (multiscale Autoregressive). Studi empiris data penumpang pesawat yang digunakan menunjukkan bahwa prosedur yang dikembangkan tidak baik dalam pembentukan model NN-MAR cocok untuk peramalan data time series musiman. Perbandingan ketepatan ramalan menunjukkan bahwa model NN-MAR harus digunakan sebagai model untuk peramalan data karena data nonlinier.

scholarly journals Improving Web QoE Monitoring for Encrypted Network Traffic through Time Series Modeling

2021 ◽  
Vol 48 (4) ◽  
pp. 37-40
Author(s):  
Nikolas Wehner ◽  
Michael Seufert ◽  
Joshua Schuler ◽  
Sarah Wassermann ◽  
Pedro Casas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Recurrent Neural Networks ◽  
Quality Of Experience ◽  
Unique Feature ◽  
Time Series Modeling ◽  
Web Browsing ◽  
Large Dataset ◽  
Efficient Approximation

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

MULTIVARIATE SPECTRAL ANALYSIS USING HILBERT WAVELET PAIRS

2004 ◽  
Vol 02 (04) ◽  
pp. 567-587 ◽  
Author(s):  
BRANDON WHITCHER ◽  
PETER F. CRAIGMILE
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Multivariate Time Series ◽  
Discrete Wavelet ◽  
Pass Filter ◽  
Wavelet Filters ◽  
The Hilbert Transform ◽  
High Pass

We investigate the use of Hilbert wavelet pairs (HWPs) in the non-decimated discrete wavelet transform for the time-varying spectral analysis of multivariate time series. HWPs consist of two high-pass and two low-pass compactly supported filters, such that one high-pass filter is the Hilbert transform (approximately) of the other. Thus, common quantities in the spectral analysis of time series (e.g., power spectrum, coherence, phase) may be estimated in both time and frequency. Compact support of the wavelet filters ensures that the frequency axis will be partitioned dyadically as with the usual discrete wavelet transform. The proposed methodology is used to analyze a bivariate time series of zonal (u) and meridional (v) winds over Truk Island.

A New Approach for Classification of Fault in Transmission Line with Combination of Wavelet Multi Resolution Analysis and Neural Networks

2017 ◽  
Vol 8 (1) ◽  
pp. 505
Author(s):  
Y Srinivasa Rao ◽  
G. Ravi Kumar ◽  
G. Kesava Rao
Keyword(s):  
Neural Networks ◽  
Wavelet Transform ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
New Approach ◽  
The Neural Network ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

An appropriate fault detection and classification of power system transmission line using discrete wavelet transform and artificial neural networks is performed in this paper. The analysis is carried out by applying discrete wavelet transform for obtained fault phase currents. The work represented in this paper are mainly concentrated on classification of fault and this classification is done based on the obtained energy values after applying discrete wavelet transform by taking this values as an input for the neural network. The proposed system and analysis is carried out in Matlab Simulink.

Sign in / Sign up

Export Citation Format

Share Document