scholarly journals Wavelet transforms of the time series of small wholesale prices in the agricultural sector

2021 ◽  
Vol 937 (3) ◽  
pp. 032075
Author(s):  
S Kazantsev ◽  
A Pavlov ◽  
O Chekha
Keyword(s):  
Time Series ◽  
Wavelet Analysis ◽  
Wavelet Transforms ◽  
Agricultural Sector ◽  
Daubechies Wavelets ◽  
Price Fluctuations ◽  
White Cabbage ◽  
Approximation Coefficients

Abstract The article provides a wavelet analysis of small wholesale prices for white cabbage in Rostov-on-Don from 2017 to 2020 year. Approximation coefficients show a steady trend, the detailing coefficients reflect seasonal and insignificant temporary price fluctuations. The constituent scaling approximation coefficients and the detailing components are highlighted in the form of separate graphs. The series was decomposed up to the 6th level using the Haar and Daubechies wavelets.

Identification and characterization of water quality transients using wavelet analysis. I. Wavelet analysis methodology

1997 ◽  
Vol 36 (5) ◽  
pp. 325-335 ◽  
Author(s):  
Kathleen Dohan ◽  
Paul H. Whitfield
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Wavelet Analysis ◽  
Wavelet Transforms ◽  
Transient Event ◽  
Frequency Decomposition ◽  
Analysis Methodology ◽  
Transient Events ◽  
Identification And Characterization

The methodology used in the identification and characterization of transient water quality events using wavelet transforms is described. The use of wavelets in the analysis of transient events is a distinct improvement over the methods which have been used previously, as wavelets are better suited to aperiodic processes than frequency decomposition. The methods presented here allow us to identify the location, duration and magnitude of a transient event in a water quality time series.

WAVELET ANALYSIS OF TIME-SERIES: COHERENT STRUCTURES, CHAOS AND NOISE

1999 ◽  
Vol 09 (03) ◽  
pp. 455-471 ◽  
Author(s):  
W. J. STASZEWSKI ◽  
K. WORDEN
Keyword(s):  
Time Series ◽  
Wavelet Analysis ◽  
Coherent Structures ◽  
Wavelet Transforms ◽  
Time Series Data ◽  
Series Data ◽  
Different Types ◽  
Analyze Time Series Data ◽  
Analyze Time Series ◽  
Analysis Of Time Series

The continuous and orthogonal wavelet transforms are used to analyze time-series data. The analysis involves signal decomposition into scale components using both Grossman–Morlet and Daubechies type wavelets. A number of simulated and experimental data vectors exhibiting different types of coherent structures, chaos and noise is analyzed. The study shows that wavelet analysis provides a unifying framework for the description of many phenomena in time-series.

scholarly journals Efficiency of Authored Mixed Prediction Model with Application to the Labor Market

2018 ◽  
Vol 7 (1) ◽  
pp. 46
Author(s):  
Monika Hadas-Dyduch
Keyword(s):  
Time Series ◽  
Labor Market ◽  
Wavelet Analysis ◽  
Prediction Model ◽  
Original Model ◽  
Daubechies Wavelets ◽  
Research Hypothesis ◽  
Alignment Model

The aim of this article is to evaluate the prediction of time series using a model containing wavelets. The research hypothesis is: “Models that take into account wavelets are an effective tool for predicting employment”. To verify the hypothesis, an original model was devised. The model is based on wavelet analysis with Daubechies wavelets and an exponential alignment model. The exponential alignment model been appropriately modified by the introduction of wavelet functions. The results obtained show that a model that partially includes wavelets is an effective tool in the prediction and analysis of employment

scholarly journals Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Tianle Zhou ◽  
Chaoyi Chu ◽  
Chaobin Xu ◽  
Weihao Liu ◽  
Hao Yu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Phase Space ◽  
Financial Market ◽  
Financial Time Series ◽  
Influential Factors ◽  
Series Data ◽  
Phase Point ◽  
Price Fluctuations ◽  
Financial Time

In this study, a new idea is proposed to analyze the financial market and detect price fluctuations, by integrating the technology of PSR (phase space reconstruction) and SOM (self organizing maps) neural network algorithms. The prediction of price and index in the financial market has always been a challenging and significant subject in time-series studies, and the prediction accuracy or the sensitivity of timely warning price fluctuations plays an important role in improving returns and avoiding risks for investors. However, it is the high volatility and chaotic dynamics of financial time series that constitute the most significantly influential factors affecting the prediction effect. As a solution, the time series is first projected into a phase space by PSR, and the phase tracks are then sliced into several parts. SOM neural network is used to cluster the phase track parts and extract the linear components in each embedded dimension. After that, LSTM (long short-term memory) is used to test the results of clustering. When there are multiple linear components in the m-dimension phase point, the superposition of these linear components still remains the linear property, and they exhibit order and periodicity in phase space, thereby providing a possibility for time series prediction. In this study, the Dow Jones index, Nikkei index, China growth enterprise market index and Chinese gold price are tested to determine the validity of the model. To summarize, the model has proven itself able to mark the unpredictable time series area and evaluate the unpredictable risk by using 1-dimension time series data.

scholarly journals The correlation between three teleconnections and leptospirosis incidence in the Kandy District, Sri Lanka, 2004–2019

2021 ◽  
Vol 49 (1) ◽  
Author(s):  
N. D. B. Ehelepola ◽  
Kusalika Ariyaratne ◽  
A. M. S. M. C. M. Aththanayake ◽  
Kamalanath Samarakoon ◽  
H. M. Arjuna Thilakarathna
Keyword(s):  
Time Series ◽  
Sri Lanka ◽  
Wavelet Analysis ◽  
Southern Oscillation ◽  
Population Data ◽  
Enso Modoki ◽  
Cross Correlation Analysis ◽  
Teleconnection Indices ◽  
Weather Parameters ◽  
Time Lagged

Abstract Background Leptospirosis is a bacterial zoonosis. Leptospirosis incidence (LI) in Sri Lanka is high. Infected animals excrete leptospires into the environment via their urine. Survival of leptospires in the environment until they enter into a person and several other factors that influence leptospirosis transmission are dependent upon local weather. Past studies show that rainfall and other weather parameters are correlated with the LI in the Kandy district, Sri Lanka. El Niño Southern Oscillation (ENSO), ENSO Modoki, and the Indian Ocean Dipole (IOD) are teleconnections known to be modulating rainfall in Sri Lanka. There is a severe dearth of published studies on the correlations between indices of these teleconnections and LI. Methods We acquired the counts of leptospirosis cases notified and midyear estimated population data of the Kandy district from 2004 to 2019, respectively, from weekly epidemiology reports of the Ministry of Health and Department of Census and Statistics of Sri Lanka. We estimated weekly and monthly LI of Kandy. We obtained weekly and monthly teleconnection indices data for the same period from the National Oceanic and Atmospheric Administration (NOAA) of the USA and Japan Agency for Marine-Earth Science and Technology (JAMSTEC). We performed wavelet time series analysis to determine correlations with lag periods between teleconnection indices and LI time series. Then, we did time-lagged detrended cross-correlation analysis (DCCA) to verify wavelet analysis results and to find the magnitudes of the correlations detected. Results Wavelet analysis displayed indices of ENSO, IOD, and ENSO Modoki were correlated with the LI of Kandy with 1.9–11.5-month lags. Indices of ENSO showed two correlation patterns with Kandy LI. Time-lagged DCCA results show all indices of the three teleconnections studied were significantly correlated with the LI of Kandy with 2–5-month lag periods. Conclusions Results of the two analysis methods generally agree indicating that ENSO and IOD modulate LI in Kandy by modulating local rainfall and probably other weather parameters. We recommend further studies about the ENSO Modoki and LI correlation in Sri Lanka. Monitoring for extreme teleconnection events and enhancing preventive measures during lag periods can blunt LI peaks that may follow.

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