TIME SERIES PROCESSING USING WAVELET-TRANSFORMATIONS  FOR ACCURACY INCREASE IN INFORMATION PRESENTATION

The purpose of this work is development of the method for error decrease in information presentation in telecommunication systems of monitoring by means of filtering noise and fluctuations of levels in time series counts. To solve this problem there is used a method of wavelet processing. In particular, the decrease of time series fluctuation impact is carried out by means of the computation of approximating coefficients of the n-th level which corresponds to the fulfillment of multi-level statistical processing the values of time series counts and equivalent to a signal passage through a filter of low frequencies. There was developed and investigated a simulator and its statistical parameters of processing with a wavelet transformation of time series counts. It is shown that time series wavelet processing and the application of approximation coefficients of waveletdecomposition increase the accuracy of data presentation. It is also ensured at the expense of noise component suppression through a method of thresholding upon detailing coefficients of decomposition. In the paper there are shown investigations of the dependence of approximation coefficient correlation time upon a wavelet decomposition level. There was also investigated a depression dependence of noise components of time series count fluctuations of emission at the processing with the wavelet decomposition with obtaining approximation coefficients of different levels. The fulfilled analysis of the results of different criteria application and approaches to smoothing on the basis of threshold processing the detail coefficients of wavelet decomposition has shown that at smoothing time series there will be an optimum choice of an adaptive penalty threshold level. The presented results of smoothing with an adaptive penalty threshold have shown that the signal-noise ratio increased for more than 2.53dB in comparison with the initial one.

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A Simplified Method of Trend Removal to Determine Noise Observed During a Supercapacitor’s Discharging

Fluctuation and Noise Letters ◽

10.1142/s0219477519400017 ◽

2019 ◽

Vol 18 (02) ◽

pp. 1940001 ◽

Cited By ~ 3

Author(s):

Ł. Lentka ◽

J. Smulko

Keyword(s):

Time Series ◽

Polynomial Approximation ◽

Additive Noise ◽

Statistical Parameters ◽

Noise Component ◽

Efficient Detection ◽

Mode Decomposition ◽

Simplified Method ◽

Trend Removal

In this paper, new method of trend removal is proposed. This is a simplified method based on Empirical Mode Decomposition (EMD). The method was applied for voltage time series observed during supercapacitor discharging process. It assured the determination of an additive noise component after subtracting the identified trend component. We analyzed voltage time series observed between the terminals of the supercapacitor when discharged by a loading resistance [Formula: see text]. The steps of the proposed method are presented in detail. The results are compared with the results obtained for polynomial approximation. Statistical parameters (kurtosis, skewness) of the histograms of the identified noise component were estimated to evaluate the quality of the proposed detrending method. The method was adjusted to the analyzed data by selecting a parameter of the applied envelope function of the EMD method. We conclude that the proposed method is faster and more efficient for detecting the additive noise component than the competitive polynomial approximation. The identified noise component may be used to evaluate the State of Health of tested supercapacitors and therefore requires fast algorithms with efficient detection.

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Selective statistical estimates of multi-year monthly concentrations of biogenous pollution of small rivers of the Northwest zone of the Russian Federation

Melioration and Water Management ◽

10.32962/0235-2524-2019-1-9-13 ◽

2020 ◽

pp. 9-13

Author(s):

Sergey Kovalenko

Keyword(s):

Statistical Processing ◽

Point Of View ◽

Small Rivers ◽

Statistical Parameters ◽

Monthly Data ◽

Statistical Estimates ◽

Scientific Task ◽

The Russian Federation ◽

Agricultural Areas

The management of surface watercourses is an urgent scientific task. The article presents the results of statistical processing of long-term monthly data of field observations of hydrological and hydrochemical parameters along the Upper Yerga small river in the Vologda region. Sampling estimates of statistical parameters are obtained, autocorrelation and correlation analyzes are performed. The limiting periods from the point of view of pollution for water receivers receiving wastewater from drained agricultural areas are identified.

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Prediction of Atmospheric Turbulence Characteristics for Surface Boundary Layer using Empirical Spectral Methods

Revista Brasileira de Meteorologia ◽

10.1590/0102-77863540083 ◽

2020 ◽

Author(s):

Yagya Dutta Dwivedi ◽

Vasishta Bhargava Nukala ◽

Satya Prasad Maddula ◽

Kiran Nair

Keyword(s):

Boundary Layer ◽

Time Series ◽

Surface Roughness ◽

Wind Speed ◽

Atmospheric Turbulence ◽

Surface Boundary ◽

Low Frequencies ◽

Surface Boundary Layer ◽

Power Spectral ◽

Mean Wind Speed

Abstract Atmospheric turbulence is an unsteady phenomenon found in nature and plays significance role in predicting natural events and life prediction of structures. In this work, turbulence in surface boundary layer has been studied through empirical methods. Computer simulation of Von Karman, Kaimal methods were evaluated for different surface roughness and for low (1%), medium (10%) and high (50%) turbulence intensities. Instantaneous values of one minute time series for longitudinal turbulent wind at mean wind speed of 12 m/s using both spectra showed strong correlation in validation trends. Influence of integral length scales on turbulence kinetic energy production at different heights is illustrated. Time series for mean wind speed of 12 m/s with surface roughness value of 0.05 m have shown that variance for longitudinal, lateral and vertical velocity components were different and found to be anisotropic. Wind speed power spectral density from Davenport and Simiu profiles have also been calculated at surface roughness of 0.05 m and compared with k−1 and k−3 slopes for Kolmogorov k−5/3 law in inertial sub-range and k−7 in viscous dissipation range. At high frequencies, logarithmic slope of Kolmogorov −5/3rd law agreed well with Davenport, Harris, Simiu and Solari spectra than at low frequencies.

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A CONSIDERATION ON STATISTICAL PROCESSING OF TIME SERIES SAMPLES

Journal of Japan Society of Civil Engineers Ser B3 (Ocean Engineering) ◽

10.2208/jscejoe.75.i_295 ◽

2019 ◽

Vol 75 (2) ◽

pp. I_295-I_300

Author(s):

Masataka YAMAGUCHI ◽

Yoshihiro UTSUNOMIYA ◽

Kunimitsu INOUCHI ◽

Hirokazu NONAKA ◽

Mikio HINO ◽

...

Keyword(s):

Time Series ◽

Statistical Processing

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Intervention Modeling

10.1093/oso/9780190661557.003.0005 ◽

2017 ◽

Author(s):

Richard McCleary ◽

David McDowall ◽

Bradley J. Bartos

Keyword(s):

Time Series ◽

Autocorrelation Function ◽

Moving Average ◽

Intervention Model ◽

Noise Component ◽

Autoregressive Integrated Moving Average ◽

Noise Function ◽

Modeling Strategy ◽

The Impact ◽

True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

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Time series forecasting based on wavelet decomposition and feature extraction

Neural Computing and Applications ◽

10.1007/s00521-016-2306-8 ◽

2016 ◽

Vol 28 (S1) ◽

pp. 183-195 ◽

Cited By ~ 1

Author(s):

Tianhong Liu ◽

Haikun Wei ◽

Chi Zhang ◽

Kanjian Zhang

Keyword(s):

Time Series ◽

Feature Extraction ◽

Wavelet Decomposition ◽

Time Series Forecasting

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Hydrology of inland tropical lowlands: the Kapuas and Mahakam wetlands

Hydrology and Earth System Sciences ◽

10.5194/hess-21-2579-2017 ◽

2017 ◽

Vol 21 (5) ◽

pp. 2579-2594 ◽

Cited By ~ 14

Author(s):

Hidayat Hidayat ◽

Adriaan J. Teuling ◽

Bart Vermeulen ◽

Muh Taufik ◽

Karl Kastner ◽

...

Keyword(s):

Time Series ◽

Groundwater Recharge ◽

Potential Evapotranspiration ◽

Southern Oscillation ◽

Tropical Rainfall Measuring Mission ◽

Threshold Level ◽

Phase Array ◽

Catchment Scale ◽

Tropical Regions ◽

Rainfall Estimates

Abstract. Wetlands are important reservoirs of water, carbon and biodiversity. They are typical landscapes of lowland regions that have high potential for water retention. However, the hydrology of these wetlands in tropical regions is often studied in isolation from the processes taking place at the catchment scale. Our main objective is to study the hydrological dynamics of one of the largest tropical rainforest regions on an island using a combination of satellite remote sensing and novel observations from dedicated field campaigns. This contribution offers a comprehensive analysis of the hydrological dynamics of two neighbouring poorly gauged tropical basins; the Kapuas basin (98 700 km2) in West Kalimantan and the Mahakam basin (77 100 km2) in East Kalimantan, Indonesia. Both basins are characterised by vast areas of inland lowlands. Hereby, we put specific emphasis on key hydrological variables and indicators such as discharge and flood extent. The hydroclimatological data described herein were obtained during fieldwork campaigns carried out in the Kapuas over the period 2013–2015 and in the Mahakam over the period 2008–2010. Additionally, we used the Tropical Rainfall Measuring Mission (TRMM) rainfall estimates over the period 1998–2015 to analyse the distribution of rainfall and the influence of El-Niño – Southern Oscillation. Flood occurrence maps were obtained from the analysis of the Phase Array type L-band Synthetic Aperture Radar (PALSAR) images from 2007 to 2010. Drought events were derived from time series of simulated groundwater recharge using time series of TRMM rainfall estimates, potential evapotranspiration estimates and the threshold level approach. The Kapuas and the Mahakam lake regions are vast reservoirs of water of about 1000 and 1500 km2 that can store as much as 3 and 6.5 billion m3 of water, respectively. These storage capacity values can be doubled considering the area of flooding under vegetation cover. Discharge time series show that backwater effects are highly influential in the wetland regions, which can be partly explained by inundation dynamics shown by flood occurrence maps obtained from PALSAR images. In contrast to their nature as wetlands, both lowland areas have frequent periods with low soil moisture conditions and low groundwater recharge. The Mahakam wetland area regularly exhibits low groundwater recharge, which may lead to prolonged drought events that can last up to 13 months. It appears that the Mahakam lowland is more vulnerable to hydrological drought, leading to more frequent fire occurrences than in the Kapuas basin.

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A Wavelet-Based Outlier Detection and Noise Component Analysis for GNSS Position Time Series

10.1007/1345_2020_106 ◽

2020 ◽

Author(s):

Kunpu Ji ◽

Yunzhong Shen

Keyword(s):

Time Series ◽

Outlier Detection ◽

Component Analysis ◽

Noise Component ◽

Position Time Series ◽

Gnss Position Time Series

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A Hybrid Model Based on Wavelet Decomposition-Reconstruction in Track Irregularity State Forecasting

Mathematical Problems in Engineering ◽

10.1155/2015/548720 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Chaolong Jia ◽

Lili Wei ◽

Hanning Wang ◽

Jiulin Yang

Keyword(s):

Time Series ◽

Signal Analysis ◽

Wavelet Decomposition ◽

Forecast Model ◽

Practical Significance ◽

Frequency Signal ◽

Time Frequency ◽

Simple Basis ◽

Best Fitting ◽

Track Irregularity

Wavelet is able to adapt to the requirements of time-frequency signal analysis automatically and can focus on any details of the signal and then decompose the function into the representation of a series of simple basis functions. It is of theoretical and practical significance. Therefore, this paper does subdivision on track irregularity time series based on the idea of wavelet decomposition-reconstruction and tries to find the best fitting forecast model of detail signal and approximate signal obtained through track irregularity time series wavelet decomposition, respectively. On this ideology, piecewise gray-ARMA recursive based on wavelet decomposition and reconstruction (PG-ARMARWDR) and piecewise ANN-ARMA recursive based on wavelet decomposition and reconstruction (PANN-ARMARWDR) models are proposed. Comparison and analysis of two models have shown that both these models can achieve higher accuracy.

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Thailand tourism forecasting based on a hybrid of discrete wavelet decomposition and NARX neural network

Industrial Management & Data Systems ◽

10.1108/imds-11-2015-0463 ◽

2016 ◽

Vol 116 (6) ◽

pp. 1242-1258 ◽

Cited By ~ 8

Author(s):

Ratree Kummong ◽

Siriporn Supratid

Keyword(s):

Neural Network ◽

Time Series ◽

Wavelet Decomposition ◽

Tourism Industry ◽

Relative Price ◽

Discrete Wavelet ◽

Content Type ◽

Input Factor ◽

Discrete Wavelet Decomposition ◽

Forecasting Performance

Purpose – Accurate forecast of tourist arrivals is crucial for Thailand since the tourism industry is a major economic factor of the country. However, a nonstationarity, normally consisted in nonlinear tourism time series can seriously ruin the forecasting computation. The purpose of this paper is to propose a hybrid forecasting method, namely discrete wavelet decomposition (DWD)-NARX, which combines DWD and the nonlinear autoregressive neural network with exogenous input (NARX) to cope with such nonstationarity, as a consequence, improve the effectiveness of the demand-side management activities. Design/methodology/approach – According to DWD-NARX, wavelet decomposition is executed for efficiently extracting the hidden significant, temporal features contained in the nonstationary time series. Then, each extracted feature set at a particular resolution level along with a relative price as an exogenous input factor are fed into NARX for further forecasting. Finally, the forecasting results are reconstructed. Forecasting performance measures rely on mean absolute percentage error, mean absolute error as well as mean square error. Model overfitting avoidance is also considered. Findings – The results indicate the superiority of the DWD-NARX over other efficient related neural forecasters in the cases of high forecasting performance rate as well as competently coping with model overfitting. Research limitations/implications – The scope of this study is confined to Thailand tourist arrivals forecast based on short-term projection. To resolve such limitations, future research should aim to apply the generalization capability of DWD-NARX on other domains of managerial time series forecast under long-term projection environment. However, the exogenous input factor is to be empirically revised on domain-by-domain basis. Originality/value – Few works have been implemented either to handle the nonstationarity, consisted in nonlinear, unpredictable time series, or to achieve great success on finding an appropriate and effective exogenous forecasting input. This study applies DWD to attain efficient feature extraction; then, utilizes the competent forecaster, NARX. This would comprehensively and specifically deal with the nonstationarity difficulties at once. In addition, this study finds the effectiveness of simply using a relative price, generated based on six top-ranked original tourist countries as an exogenous forecasting input.

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