scholarly journals Analysis of almost-periodic and almost-proportional characteristics of a representative sample local minima time series

2021 ◽  
Vol 1047 (1) ◽  
pp. 012045
Author(s):  
A A Paramonov ◽  
V I Kuzmin ◽  
R I Dzerjinsky
Keyword(s):  
Time Series ◽  
Representative Sample ◽  
Almost Periodic ◽  
Local Minima

scholarly journals Phenological Monitoring of Paddy Crop Using Time Series MODIS Data

Proceedings ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 19
Author(s):  
C. Dineshkumar ◽  
S. Nitheshnirmal ◽  
Ashutosh Bhardwaj ◽  
K. Nivedita Priyadarshini
Keyword(s):  
Time Series ◽  
Tamil Nadu ◽  
Vegetation Index ◽  
Google Earth ◽  
Paddy Fields ◽  
Series Data ◽  
Local Minima ◽  
Phenological Stages ◽  
Local Maxima ◽  
Modis Data

Rice is an important staple food crop worldwide, especially in India. Accurate and timely prediction of rice phenology plays a significant role in the management of water resources, administrative planning, and food security. In addition to conventional methods, remotely sensed time series data can provide the necessary estimation of rice phenological stages over a large region. Thus, the present study utilizes the 16-day composite Enhanced Vegetation Index (EVI) product with a spatial resolution of 250 m from the Moderate Resolution Imaging Spectroradiometer (MODIS) to monitor the rice phenological stages over Karur district of Tamil Nadu, India, using the Google Earth Engine (GEE) platform. The rice fields in the study area were classified using the machine learning algorithm in GEE. The ground truth was obtained from the paddy fields during crop production which was used for classifying the paddy grown area. After the classification of paddy fields, local maxima, and local minima present in each pixel of time series, the EVI product was used to determine the paddy growing stages in the study area. The results show that in the initial stage the pixel value of EVI in the paddy field shows local minima (0.23), whereas local maxima (0.41) were obtained during the peak vegetative stage. The results derived from the present study using MODIS data were cross-validated using the field data.

A COMPARATIVE STUDY OF MODEL SELECTION METHODS FOR NONLINEAR TIME SERIES

2004 ◽  
Vol 14 (03) ◽  
pp. 1129-1146 ◽  
Author(s):  
TOMOMICHI NAKAMURA ◽  
DEVIN KILMINSTER ◽  
KEVIN JUDD ◽  
ALISTAIR MEES
Keyword(s):  
Time Series ◽  
Simulated Annealing ◽  
Computation Time ◽  
Nonlinear Time Series ◽  
Np Hard ◽  
Local Minima ◽  
Selection Methods ◽  
New Methods ◽  
Sunspot Data ◽  
Hard Problems

Constructing models of nonlinear time series is typically NP-hard. One of the difficulties is the local minima, and it is difficult to find a global best model. Some methods have already been proposed that attempt to find good models with reasonable computation time. In this paper we propose new methods that can compensate for a drawback of a method previously proposed by Judd and Mees. A standard approach to NP-hard problems is simulated annealing. We apply these methods to build models of annual sunspot numbers and a laser time series, and compare the results. The results indicate that the performance of the proposed method is comparable to that of simulated annealing in both time series. The performance of Judd and Mees method is almost the same as that of the other methods for the annual sunspot data, but not as good for laser time series. The Judd and Mees method is computationally the fastest of all the methods, and the proposed method is faster than simulated annealing.

Nonparametric Band Business Cycle Clock

2016 ◽  
Vol 63 (4) ◽  
pp. 375-390
Author(s):  
Łukasz Lenart ◽  
Mateusz Pipień
Keyword(s):  
Phase Diagram ◽  
Time Series ◽  
Business Cycle ◽  
Almost Periodic Function ◽  
Almost Periodic ◽  
Seasonal Fluctuations ◽  
Period Function ◽  
Business Fluctuations ◽  
Trend Component ◽  
The Business Cycle

We discuss representation of uncertainty in the business cycle clock. We propose approach utilising description of the unconditional mean of the process, applied for modelling dynamics of macroeco-nomic time series, as a trend component and almost period function in a non-parametric setting. We capture the dynamics over the business cycle, trend component and seasonal fluctuations and possible interactions between these features. A particular values of the almost periodic function are key for representation of the business cycle in a clock, expressing the dynamics according to phase diagram. The set of frequencies interpreted as a properties of the business fluctuations are invariant with respect to filtration methods applied in the procedure.

On bayesian inference for almost periodic in mean autoregressive models

2016 ◽  
Vol 63 (3) ◽  
pp. 255-272
Author(s):  
Łukasz Lenart ◽  
Błażej Mazur
Keyword(s):  
Time Series ◽  
Bayesian Estimation ◽  
Real Data ◽  
Approximate Model ◽  
Model Parameters ◽  
Almost Periodic ◽  
Cyclical Fluctuations ◽  
Univariate Time Series ◽  
Mean Function ◽  
Mcmc Chain

The goal of the paper is to discuss Bayesian estimation of a class of univariate time-series models being able to represent complicated patterns of “cyclical” fluctuations in mean function. We highlight problems that arise in Bayesian estimation of parametric time-series model using the Flexible Fourier Form of Gallant (1981). We demonstrate that the resulting posterior is likely to be highly multimodal, therefore standard Markov Chain Monte Carlo (MCMC in short) methods might fail to explore the whole posterior, especially when the modes are separated. We show that the multimodality is actually an issue using the exact solution (i.e. an analytical marginal posterior) in an approximate model. We address that problem using two essential steps. Firstly, we integrate the posterior with respect to amplitude parameters, which can be carried out analytically. Secondly, we propose a non-parametrically motivated proposal for the frequency parameters. This allows for construction of an improved MCMC sampler that effectively explores the space of all the model parameters, with the amplitudes sampled by the direct approach outside the MCMC chain. We illustrate the problem using simulations and demonstrate our solution using two real-data examples.

scholarly journals Magnetic fields in classical Be stars: results of our long-term program with FORS1 at the VLT

2010 ◽  
Vol 6 (S272) ◽  
pp. 222-223
Author(s):  
Ruslan V. Yudin ◽  
Swetlana Hubrig ◽  
Michail A. Pogodin ◽  
Markus Schoeller
Keyword(s):  
Magnetic Field ◽  
Time Series ◽  
Magnetic Fields ◽  
Representative Sample ◽  
Be Stars ◽  
Low Resolution ◽  
Cyclic Variability ◽  
Be Star ◽  
The Magnetic Field

AbstractWe report the results of our search for magnetic fields in a representative sample of classical Be stars carried out during 2006-2008 using low-resolution spectropolarimetry with FORS1 at the VLT. Among the 28 classical Be stars studied, detections of a magnetic field were achieved in seven stars (i.e. ~25%). The detected magnetic fields are rather weak, not stronger than ~150G. Among the Be stars studied with time series, one Be star, λ Eri, displays cyclic variability of the magnetic field with a period of 21.12 min.

scholarly journals The impact of measles immunization campaigns in India using a nationally representative sample of 27,000 child deaths

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Benjamin KC Wong ◽  
Shaza A Fadel ◽  
Shally Awasthi ◽  
Ajay Khera ◽  
Rajesh Kumar ◽  
...  
Keyword(s):  
Time Series ◽  
Mortality Risk ◽  
Representative Sample ◽  
Interrupted Time Series ◽  
Mortality Rates ◽  
Multilevel Regression ◽  
Child Deaths ◽  
Nationally Representative ◽  
The Impact ◽  
Immunization Campaign

India comprises much of the persisting global childhood measles mortality. India implemented a mass second-dose measles immunization campaign in 2010. We used interrupted time series and multilevel regression to quantify the campaign’s impact on measles mortality using the nationally representative Million Death Study (including 27,000 child deaths in 1.3 million households surveyed from 2005 to 2013). 1–59 month measles mortality rates fell more in the campaign states following launch (27%) versus non-campaign states (11%). Declines were steeper in girls than boys and were specific to measles deaths. Measles mortality risk was lower for children living in a campaign district (OR 0.6, 99% CI 0.4–0.8) or born in 2009 or later (OR 0.8, 99% CI 0.7–0.9). The campaign averted up to 41,000–56,000 deaths during 2010–13, or 39–57% of the expected deaths nationally. Elimination of measles deaths in India is feasible.

NOISE REDUCTION BY GRADIENT DESCENT

1993 ◽  
Vol 03 (01) ◽  
pp. 113-118 ◽  
Author(s):  
MIKE DAVIES
Keyword(s):  
Dynamical System ◽  
Time Series ◽  
Gradient Descent ◽  
Nonlinear Dynamical System ◽  
Descent Method ◽  
Steepest Descent Method ◽  
Gradient Descent Method ◽  
Local Minima ◽  
Nonlinear Dynamical ◽  
The Stability

The problem of reducing noise in a time series from a nonlinear dynamical system can be formulated as a nonlinear minimisation process. This paper demonstrates that this can be easily solved using a steepest descent method without any of the stability problems that have been associated with using a Newton method [Hammel, 1990; Farmer & Sidorowich, 1991]. The optimisation function to be minimised is also shown not to contain any local minima if the trajectory is always hyperbolic. So that in this case this method will converge eventually to a purely deterministic trajectory. Finally this method is compared with a recently proposed algorithm [Schreiber & Grassberger, 1991], which can be viewed as an alternative gradient descent method.

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