scholarly journals Low-Pass Filtering Method for Poisson Data Time Series

2021 ◽  
Vol 11 (10) ◽  
pp. 4524
Author(s):  
Victor Getmanov ◽  
Vladislav Chinkin ◽  
Roman Sidorov ◽  
Alexei Gvishiani ◽  
Mikhail Dobrovolsky ◽  
...  
Keyword(s):  
Time Series ◽  
Finite Impulse Response ◽  
Digital Processing ◽  
Distributed Data ◽  
Gaussian Filter ◽  
Experimental Physics ◽  
Filtering Method ◽  
Filter Synthesis ◽  
Low Pass ◽  
Poisson Data

Problems of digital processing of Poisson-distributed data time series from various counters of radiation particles, photons, slow neutrons etc. are relevant for experimental physics and measuring technology. A low-pass filtering method for normalized Poisson-distributed data time series is proposed. A digital quasi-Gaussian filter is designed, with a finite impulse response and non-negative weights. The quasi-Gaussian filter synthesis is implemented using the technology of stochastic global minimization and modification of the annealing simulation algorithm. The results of testing the filtering method and the quasi-Gaussian filter on model and experimental normalized Poisson data from the URAGAN muon hodoscope, that have confirmed their effectiveness, are presented.

scholarly journals VISUAL ANALYSIS OF RECURRENCE OF TIME SERIES OF THE COORDINATES ENU IN THE GPS STATIONS

2018 ◽  
Vol 24 (4) ◽  
pp. 470-484
Author(s):  
Alfonso Tierra ◽  
Rubén León ◽  
Alexis Tinoco-S ◽  
Carolina Cañizares ◽  
Marco Amores ◽  
...  
Keyword(s):  
Time Series ◽  
Continuous Monitoring ◽  
Visual Analysis ◽  
Finite Impulse Response ◽  
Satellite System ◽  
Nearest Neighbours ◽  
Low Pass ◽  
Global Navigation Satellite ◽  
Low Pass Filters ◽  
Content Information

Abstract The time series content information about the dynamic behavior of the system under study. This behavior could be complex, irregular and no lineal. For this reason, it is necessary to study new models that can solve this dynamic more satisfactorily. In this work a visual analysis of recurrence from time series of the coordinate’s variation ENU (East, North, Up) will be made. This analysis was obtained from nine continuous monitoring stations GPS (Global Navigation Satellite System); the intention is to study their behavior, they belong to the Equatorian GPS Network that materializes the reference system SIRGAS - ECUADOR. The presence of noise in the observations was reduced using digital low pass filters with Finite Impulse Response (FIR). For these series, the time delay was determined using the average mutual information, and for the minimum embedding dimension the False Nearest Neighbours (FNN) method was used; the purpose is to obtain the recurrent maps of each coordinates. The results of visual analysis show a strong tendency, especially in the East and North coordinates, while the Up coordinates indicate discontinued, symmetric and periodic behavior.

CONSTRAINED FORMULATIONS AND ALGORITHMS FOR PREDICTING STOCK PRICES BY RECURRENT FIR NEURAL NETWORKS

2006 ◽  
Vol 05 (04) ◽  
pp. 639-658 ◽  
Author(s):  
BENJAMIN W. WAH ◽  
MING-LUN QIAN
Keyword(s):  
Time Series ◽  
Constrained Optimization ◽  
Stock Prices ◽  
Finite Impulse Response ◽  
Saddle Points ◽  
Stationary Time Series ◽  
Good Prediction ◽  
Learning Problem ◽  
Good Prediction Accuracy ◽  
Low Pass

In this paper, we develop a new constrained artificial-neural-network (ANN) formulation and the associated learning algorithm for predicting stock prices, a difficult time-series prediction problem. We characterize daily stock prices as a noisy non-stationary time series and identify its predictable low-frequency components. Using a recurrent finite-impulse-response ANN, we formulate the learning problem as a constrained optimization problem, develop constraints for incorporating cross validations, and solve the learning problem using algorithms based on the theory of extended saddle points for nonlinear constrained optimization. Finally, we illustrate our prediction results on ten stock-price time series. Our main contributions in this paper are the channel-specific low-pass filtering of noisy time series obtained by wavelet decomposition, the transformation of the low-pass signals to improve their stationarity, and the incorporation of constraints on cross validation that can improve the accuracy of predictions. Our experimental results demonstrate good prediction accuracy and annual returns.

SYNTHESIS OF A DIGITAL LOW-PASS QUASI-GAUSSIAN FILTER FOR NOISE REDUCTION IN POISSON OBSERVATIONS

2021 ◽  
Vol 57 (4) ◽  
pp. 118-125
Author(s):  
V. E. Chinkin ◽  
V. G. Getmanov ◽  
I. I. Yashin
Keyword(s):  
Noise Reduction ◽  
Gaussian Filter ◽  
Low Pass

scholarly journals A Comparison of Denoising Methods in Onset Determination in Medial Gastrocnemius Muscle Activations during Stance

Sci ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 53
Author(s):  
Jian Zhang ◽  
Rahul Soangra ◽  
Thurmon E. Lockhart
Keyword(s):  
Time Delay ◽  
Empirical Mode Decomposition ◽  
Gastrocnemius Muscle ◽  
Wavelet Denoising ◽  
Filtering Method ◽  
Mode Decomposition ◽  
Low Pass ◽  
Eemd Method ◽  
Muscle Activations ◽  
Stance Duration

One of the most basic pieces of information gained from dynamic electromyography is accurately defining muscle action and phase timing within the gait cycle. The human gait relies on selective timing and the intensity of appropriate muscle activations for stability, loading, and progression over the supporting foot during stance, and further to advance the limb in the swing phase. A common clinical practice is utilizing a low-pass filter to denoise integrated electromyogram (EMG) signals and to determine onset and cessation events using a predefined threshold. However, the accuracy of the defining period of significant muscle activations via EMG varies with the temporal shift involved in filtering the signals; thus, the low-pass filtering method with a fixed order and cut-off frequency will introduce a time delay depending on the frequency of the signal. In order to precisely identify muscle activation and to determine the onset and cessation times of the muscles, we have explored here onset and cessation epochs with denoised EMG signals using different filter banks: the wavelet method, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method. In this study, gastrocnemius muscle onset and cessation were determined in sixteen participants within two different age groups and under two different walking conditions. Low-pass filtering of integrated EMG (iEMG) signals resulted in premature onset (28% stance duration) in younger and delayed onset (38% stance duration) in older participants, showing the time-delay problem involved in this filtering method. Comparatively, the wavelet denoising approach detected onset for normal walking events most precisely, whereas the EEMD method showed the smallest onset deviation. In addition, EEMD denoised signals could further detect pre-activation onsets during a fast walking condition. A comprehensive comparison is discussed on denoising EMG signals using EMD, EEMD, and wavelet denoising in order to accurately define an onset of muscle under different walking conditions.

scholarly journals A geometric method of analysis for the light curves of active galactic nuclei

2020 ◽  
Author(s):  
Isao Shoji ◽  
Tadafumi Takata ◽  
Yoshihiko Mizumoto
Keyword(s):  
Time Series ◽  
Vector Field ◽  
Active Galactic Nuclei ◽  
Dynamic Properties ◽  
Flux Ratio ◽  
Galactic Nuclei ◽  
Light Curves ◽  
Geometric Method ◽  
Function Form ◽  
Filtering Method

Abstract We propose a geometric method of analysis for the light curves of active galactic nuclei (AGN). The time series of flux ratio is modeled by possibly nonlinear random oscillation without specifying the function form. Based on the model, we map the dynamic behavior of flux ratio to a vector field on a manifold, and then analyze the vector field to retrieve information on the dynamic properties closely linked with the activity of AGN. While the function form of the model is unspecified, the vector fields and those associated quantities can be estimated by applying a nonparametric filtering method. We illustrate the proposed analysis with an application to light curves of two AGNs supplied by the Kepler satellite. The application shows that the vector field, its derivative and their combination will be used as the tools of picking up various signals that help understanding of the activity of AGN. In addition, from a technical viewpoint, the nonparametric filtering method allows the estimation to be robust against outliers. The proposed analysis could be used as alternative time series analysis of the optical variability other than the analysis by spectral densities or structure functions.

scholarly journals Continuous partial trends and low-frequency oscillations of time series

2005 ◽  
Vol 12 (4) ◽  
pp. 451-460 ◽  
Author(s):  
A. R. Tomé ◽  
P. M. A. Miranda
Keyword(s):  
Time Series ◽  
Low Frequency ◽  
Distributed Data ◽  
Spatially Distributed ◽  
Geophysical Time Series ◽  
Slow Evolution ◽  
Using Data ◽  
The Times ◽  
Data Subject

Abstract. This paper presents a recent methodology developed for the analysis of the slow evolution of geophysical time series. The method is based on least-squares fitting of continuous line segments to the data, subject to flexible conditions, and is able to objectively locate the times of significant change in the series tendencies. The time distribution of these breakpoints may be an important set of parameters for the analysis of the long term evolution of some geophysical data, simplifying the intercomparison between datasets and offering a new way for the analysis of time varying spatially distributed data. Several application examples, using data that is important in the context of global warming studies, are presented and briefly discussed.

scholarly journals A Comparison of Denoising Methods in Onset Determination in Medial Gastrocnemius Muscle Activations during Stance

Sci ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 39 ◽  
Author(s):  
Jian Zhang ◽  
Rahul Soangra ◽  
Thurmon E. Lockhart
Keyword(s):  
Time Delay ◽  
Empirical Mode Decomposition ◽  
Gastrocnemius Muscle ◽  
Wavelet Denoising ◽  
Filtering Method ◽  
Mode Decomposition ◽  
Low Pass ◽  
Eemd Method ◽  
Muscle Activations ◽  
Stance Duration

One of the most basic pieces of information gained from dynamic electromyography is accurately defining muscle action and phase timing within the gait cycle. The human gait relies on selective timing and the intensity of appropriate muscle activations for stability, loading, and progression over the supporting foot during stance, and further to advance the limb in the swing phase. A common clinical practice is utilizing a low-pass filter to denoise integrated electromyogram (EMG) signals and to determine onset and cessation events using a predefined threshold. However, the accuracy of the defining period of significant muscle activations via EMG varies with the temporal shift involved in filtering the signals; thus, the low-pass filtering method with a fixed order and cut-off frequency will introduce a time delay depending on the frequency of the signal. In order to precisely identify muscle activation and to determine the onset and cessation times of the muscles, we have explored here onset and cessation epochs with denoised EMG signals using different filter banks: the wavelet method, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method. In this study, gastrocnemius muscle onset and cessation were determined in sixteen participants within two different age groups and under two different walking conditions. Low-pass filtering of integrated EMG (iEMG) signals resulted in premature onset (28% stance duration) in younger and delayed onset (38% stance duration) in older participants, showing the time-delay problem involved in this filtering method. Comparatively, the wavelet denoising approach detected onset for normal walking events most precisely, whereas the EEMD method showed the smallest onset deviation. In addition, EEMD denoised signals could further detect pre-activation onsets during a fast walking condition. A comprehensive comparison is discussed on denoising EMG signals using EMD, EEMD, and wavelet denoising in order to accurately define an onset of muscle under different walking conditions.

scholarly journals A Speckle Filtering Method Based on Hypothesis Testing for Time-Series SAR Images

2018 ◽  
Vol 10 (9) ◽  
pp. 1383 ◽  
Author(s):  
Jili Yuan ◽  
Xiaolei Lv ◽  
Rui Li
Keyword(s):  
Time Series ◽  
Hypothesis Testing ◽  
Speckle Noise ◽  
Sar Images ◽  
Suppression Effect ◽  
Filtering Method ◽  
Information Preservation ◽  
Spatiotemporal Information ◽  
Test Algorithm ◽  
Filtering Effect

To improve the suppression effect for the speckle noise of synthetic aperture radar (SAR) images and the ability of spatiotemporal information preservation of the filtered image without losing the spatial resolution, a novel multitemporal filtering method based on hypothesis testing is proposed in this paper. A framework of a two-step similarity measure strategy is adopted to further enhance the filtering results. Firstly, bi-date analysis using a two-sample Kolmogorov-Smirnov (KS) test is conducted in step 1 to extract homogeneous patches for 3-D patch stacks generation. Subsequently, the similarity between patch stacks is compared by a sliding time-series likelihood ratio (STSLR) test algorithm in step 2, which utilizes the multi-dimensional data structure of the stacks to improve the accuracy of unchanged pixels detection. Finally, the filtered values are obtained by averaging the similar pixels in time-series. The experimental results and analysis of two multitemporal datasets acquired by TerraSAR-X show that the proposed method outperforms the other typical methods with regard to the overall filtering effect, especially in terms of the consistency between the filtered images and the original ones. Furthermore, the performance of the proposed method is also discussed by analyzing the results from step 1 and step 2.

scholarly journals Medical service demand forecasting using a hybrid model based on ARIMA and self-adaptive filtering method

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yihuai Huang ◽  
Chao Xu ◽  
Mengzhong Ji ◽  
Wei Xiang ◽  
Da He
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Adaptive Filtering ◽  
Time Series Data ◽  
Arima Model ◽  
Series Data ◽  
Short Term ◽  
Hybrid Prediction ◽  
Service Demand ◽  
Filtering Method

Abstract Background Accurate forecasting of medical service demand is beneficial for the reasonable healthcare resource planning and allocation. The daily outpatient volume is characterized by randomness, periodicity and trend, and the time series methods, like ARIMA are often used for short-term outpatient visits forecasting. Therefore, to further enlarge the prediction horizon and improve the prediction accuracy, a hybrid prediction model integrating ARIMA and self-adaptive filtering method is proposed. Methods The ARIMA model is first used to identify the features like cyclicity and trend of the time series data and to estimate the model parameters. The parameters are then adjusted by the steepest descent algorithm in the adaptive filtering method to reduce the prediction error. The hybrid model is validated and compared with traditional ARIMA by several test sets from the Time Series Data Library (TSDL), a weekly emergency department (ED) visit case from literature study, and the real cases of prenatal examinations and B-ultrasounds in a maternal and child health care center (MCHCC) in Ningbo. Results For TSDL cases the prediction accuracy of the hybrid prediction is improved by 80–99% compared with the ARIMA model. For the weekly ED visit case, the forecasting results of the hybrid model are better than those of both traditional ARIMA and ANN model, and similar to the ANN combined data decomposition model mentioned in the literature. For the actual data of MCHCC in Ningbo, the MAPE predicted by the ARIMA model in the two departments was 18.53 and 27.69%, respectively, and the hybrid models were 2.79 and 1.25%, respectively. Conclusions The hybrid prediction model outperforms the traditional ARIMA model in both accurate predicting result with smaller average relative error and the applicability for short-term and medium-term prediction.

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