Improvement of Chaotic Signals De-Noising with the Self-Optimizing Method of Wavelet Threshold

2014 ◽  
Vol 556-562 ◽  
pp. 4950-4954
Author(s):  
Xiu Lei Wei ◽  
Rui Lin Lin ◽  
Shu Yong Liu ◽  
Qiang Wang
Keyword(s):  
Time Series ◽  
Adaptive Filters ◽  
Cross Validation ◽  
Memetic Algorithm ◽  
Absolute Error ◽  
Global Optimum ◽  
The Self ◽  
Adaptive Performance ◽  
Adaptive Wavelet ◽  
Chaotic Signals

For the purpose of improving adaptive performance of chaotic signals de-noising with wavelet transform, a method of Memetic-algorithm-based adaptive wavelet de-noising (MAWD) is presented. The MAWD based on generalized cross validation (GCV) is competent to obtain the global optimum thresholds and to raise the efficiency of adaptive searching computation. The de-noising results of simulative Lorenz time series are presented. The results show that the chaotic signals de-noised by MAWD can remove the white noise more effectively than the signals de-noised by using standard soft threshoding method (STM) and genetic-algorithm-based adaptive wavelet de-noising (GAWD), and the advantages are more apparent under the condition of lower SNR. The Lorenz time series with lower SNR de-noised by MAWD and GAWD respectively are predicted by Volterra adaptive filters, and the results show that the prediction absolute error of Lorenz time series de-noised by MAWD is nearly nine times smaller than that by GAWD. This method has a promising prospect in practical Chaotic signals de-noising.

scholarly journals On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study

2021 ◽  
Vol 13 (24) ◽  
pp. 13599
Author(s):  
Dalton Garcia Borges de Souza ◽  
Erivelton Antonio dos Santos ◽  
Francisco Tarcísio Alves Júnior ◽  
Mariá Cristina Vasconcelos Nascimento
Keyword(s):  
Time Series ◽  
Cross Validation ◽  
Absolute Error ◽  
Fuzzy Topsis ◽  
Decision Makers ◽  
Similarity Metric ◽  
Forecasting Models ◽  
Fuzzy Similarity ◽  
Relative Comparisons

Time series cross-validation is a technique to select forecasting models. Despite the sophistication of cross-validation over single test/training splits, traditional and independent metrics, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), are commonly used to assess the model’s accuracy. However, what if decision-makers have different models fitting expectations to each moment of a time series? What if the precision of the forecasted values is also important? This is the case of predicting COVID-19 in Amapá, a Brazilian state in the Amazon rainforest. Due to the lack of hospital capacities, a model that promptly and precisely responds to notable ups and downs in the number of cases may be more desired than average models that only have good performances in more frequent and calm circumstances. In line with this, this paper proposes a hybridization of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and fuzzy sets to create a similarity metric, the closeness coefficient (CC), that enables relative comparisons of forecasting models under heterogeneous fitting expectations and also considers volatility in the predictions. We present a case study using three parametric and three machine learning models commonly used to forecast COVID-19 numbers. The results indicate that the introduced fuzzy similarity metric is a more informative performance assessment metric, especially when using time series cross-validation.

On the use of cross-validation for time series predictor evaluation

2012 ◽  
Vol 191 ◽  
pp. 192-213 ◽  
Author(s):  
Christoph Bergmeir ◽  
José M. Benítez
Keyword(s):  
Time Series ◽  
Cross Validation

Prediction of Surge Motion Response of Floating Structure Using Kalman Smoother Adaptive Filters Based Time-Domain Volterra Model

2014 ◽  
Vol 660 ◽  
pp. 799-803
Author(s):  
Edwar Yazid ◽  
M.S. Liew ◽  
Setyamartana Parman ◽  
V.J. Kurian ◽  
C.Y. Ng
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Time Domain ◽  
Adaptive Filters ◽  
Low Frequency ◽  
Volterra Model ◽  
Floating Structure ◽  
Frequency Responses ◽  
Kalman Smoother ◽  
System Output

This work presents an approachto predict the low frequency and wave frequency responses (LFR and WFR) of afloating structure using Kalman smoother adaptive filters based time domain Volterramodel. This method utilized time series of a measured wave height as systeminput and surge motion as system output and used to generate the linear andnonlinear transfer function (TFs). Based on those TFs, predictions of surgemotion in terms of LFR and WFR were carried out in certain frequency ranges ofwave heights. The applicability of the proposed method is then applied in ascaled 1:100 model of a semisubmersible prototype.

Time-Series Cross-Validation Parallel Programming using MPI

2021 ◽  
Author(s):  
Abdulrahim Mohammed ◽  
Ahmed Khedr ◽  
Duaa AlHaj ◽  
Reem Al Khalifa ◽  
Abdulla Alqaddoumi
Keyword(s):  
Time Series ◽  
Parallel Programming ◽  
Cross Validation

New Nonlinear Adaptive Filters with Applications to Chaos

1997 ◽  
Vol 07 (08) ◽  
pp. 1791-1809 ◽  
Author(s):  
Fawad Rauf ◽  
Hassan M. Ahmed
Keyword(s):  
Adaptive Filters ◽  
Broad Class ◽  
Chaotic Map ◽  
New Class ◽  
Chaotic Signals ◽  
Data Points ◽  
Modeling Prediction ◽  
Polynomial Filters ◽  
And Performance ◽  
Nonlinear Adaptive Filtering

We present a new approach to nonlinear adaptive filtering based on Successive Linearization. Our approach provides a simple, modular and unified implementation for a broad class of polynomial filters. We refer to this implementation as the layered structure and note that it offers substantial computational efficiency over previous methods. A new class of Polynomial Autoregressive filters is introduced which can model limit cycle and chaotic dynamics. Existing geometric methods for modeling and characterizing chaotic processes suffer from several drawbacks. They require a huge number of data points to reconstruct the attractor geometry and performance is severely limited by noisy experimental measurements. We present a new method for processing chaotic signals using nonlinear adaptive filters. We demonstrate the modeling, prediction and filtering of these signals. We also show how the prediction error growth rate can be used to estimate the effective Lyapunov exponent of the chaotic map. Our approach requires orders of magnitude fewer data points and is robust to noise in the experimental data. Although reconstruction of the attractor geometry is unnecessary, the adaptive filter contains most of the geometric information.

Network reconstruction based on time series via memetic algorithm

2019 ◽  
Vol 164 ◽  
pp. 404-425 ◽  
Author(s):  
Kai Wu ◽  
Jing Liu ◽  
Dan Chen
Keyword(s):  
Time Series ◽  
Memetic Algorithm ◽  
Network Reconstruction

scholarly journals Forecasting Reservoir Water Levels Using Deep Neural Networks: A Case Study of Angat Dam in the Philippines

Water ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 34
Author(s):  
Sebastian C. Ibañez ◽  
Carlo Vincienzo G. Dajac ◽  
Marissa P. Liponhay ◽  
Erika Fille T. Legara ◽  
Jon Michael H. Esteban ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Neural Networks ◽  
Cross Validation ◽  
The Philippines ◽  
Water Levels ◽  
Short Term ◽  
Exogenous Variables ◽  
Reservoir Water ◽  
Forecasting Performance

Forecasting reservoir water levels is essential in water supply management, impacting both operations and intervention strategies. This paper examines the short-term and long-term forecasting performance of several statistical and machine learning-based methods for predicting the water levels of the Angat Dam in the Philippines. A total of six forecasting methods are compared: naïve/persistence; seasonal mean; autoregressive integrated moving average (ARIMA); gradient boosting machines (GBM); and two deep neural networks (DNN) using a long short-term memory-based (LSTM) encoder-decoder architecture: a univariate model (DNN-U) and a multivariate model (DNN-M). Daily historical water levels from 2001 to 2021 are used in predicting future water levels. In addition, we include meteorological data (rainfall and the Oceanic Niño Index) and irrigation data as exogenous variables. To evaluate the forecast accuracy of our methods, we use a time series cross-validation approach to establish a more robust estimate of the error statistics. Our results show that our DNN-U model has the best accuracy in the 1-day-ahead scenario with a mean absolute error (MAE) and root mean square error (RMSE) of 0.2 m. In the 30-day-, 90-day-, and 180-day-ahead scenarios, the DNN-M shows the best performance with MAE (RMSE) scores of 2.9 (3.3), 5.1 (6.0), and 6.7 (8.1) meters, respectively. Additionally, we demonstrate that further improvements in performance are possible by scanning over all possible combinations of the exogenous variables and only using a subset of them as features. In summary, we provide a comprehensive framework for evaluating water level forecasting by defining a baseline accuracy, analyzing performance across multiple prediction horizons, using time series cross-validation to assess accuracy and uncertainty, and examining the effects of exogenous variables on forecasting performance. In the process, our work addresses several notable gaps in the methodologies of previous works.

scholarly journals SUBSIDENCE EVALUATION OF HIGH-SPEED RAILWAY IN SHENYANG BASED ON TIME-SERIES INSAR

2018 ◽  
Vol IV-3 ◽  
pp. 273-276
Author(s):  
Yun Zhang ◽  
Lianhuan Wei ◽  
Jiayu Li ◽  
Shanjun Liu ◽  
Yachun Mao ◽  
...  
Keyword(s):  
Time Series ◽  
High Speed ◽  
Cross Validation ◽  
Surface Deformation ◽  
Local Environment ◽  
Local Area ◽  
Safe Operation ◽  
High Speed Rail ◽  
High Speed Railway ◽  
Under Construction

More and more high-speed railway are under construction in China. The slow settlement along high-speed railway tracks and newly-built stations would lead to inhomogeneous deformation of local area, and the accumulation may be a threat to the safe operation of high-speed rail system. In this paper, surface deformation of the newly-built high-speed railway station as well as the railway lines in Shenyang region will be retrieved by time series InSAR analysis using multi-orbit COSMO-SkyMed images. This paper focuses on the non-uniform subsidence caused by the changing of local environment along the railway. The accuracy of the settlement results can be verified by cross validation of the results obtained from two different orbits during the same period.

Sign in / Sign up

Export Citation Format

Share Document