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You searched for: Subject "time series analysis" Remove constraint Subject: "time series analysis"
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1. Impacts of meteorology and precursor emission change on O3 variation in Tianjin, China from 2015 to 2021

Author:
Jing Ding; Qili Dai; Wenyan Fan; Miaomiao Lu; Yufen Zhang; Suqin Han; Yinchang Feng
Source:
Journal of environmental sciences (China) 2023 v.126 pp. 506-516
ISSN:
1001-0742
Subject:
time series analysis,   etc   ; air; algorithms; humidity; loess; meteorology; ozone; pollution; summer; temperature; China;     Show all 11 Subjects
Abstract:
... Deterioration of surface ozone (O₃) pollution in Northern China over the past few years received much attention. For many cities, it is still under debate whether the trend of surface O₃ variation is driven by meteorology or the change in precursors emissions. In this work, a time series decomposition method (Seasonal-Trend decomposition procedure based on Loess (STL)) and random forest (RF) algor ...
DOI:
10.1016/j.jes.2022.03.010

2. A comparison of single and multiple changepoint techniques for time series data

Author:
Xuesheng Shi; Colin Gallagher; Robert Lund; Rebecca Killick
Source:
Computational statistics & data analysis 2022 v.170 pp. 107433
ISSN:
0167-9473
Subject:
time series analysis,   etc   ; statistics; testing;     Show all 3 Subjects
Abstract:
... Correlated time series data arise in many applications. This paper describes and compares several prominent single and multiple changepoint techniques for correlated time series. In the single changepoint problem, various cumulative sum (CUSUM) and likelihood ratio statistics, along with boundary cropping scenarios and scaling methods (e.g., scaling to an extreme value or Brownian Bridge limit) ar ...
DOI:
10.1016/j.csda.2022.107433

3. Model identification via total Frobenius norm of multivariate spectra

Author:
Tucker S. McElroy; Anindya Roy
Source:
Journal of the Royal Statistical Society 2022 v.84 no.2 pp. 473-495
ISSN:
1369-7412
Subject:
time series analysis,   etc   ; inflation; models;     Show all 3 Subjects
Abstract:
... We study the integral of the Frobenius norm as a measure of the discrepancy between two multivariate spectra. Such a measure can be used to fit time series models, and ensures proximity between model and process at all frequencies of the spectral density—this is more demanding than Kullback–Leibler discrepancy, which is instead related to one‐step ahead forecasting performance. We develop new asym ...
DOI:
10.1111/rssb.12480

4. Robust likelihood estimation of dynamic panel data models

Author:
Javier Alvarez; Manuel Arellano
Source:
Journal of econometrics 2022 v.226 no.1 pp. 21-61
ISSN:
0304-4076
Subject:
time series analysis,   etc   ; heteroskedasticity; income;     Show all 3 Subjects
Abstract:
... We develop likelihood-based estimators for autoregressive panel data models that are consistent in the presence of time series heteroskedasticity. Bias-corrected conditional score estimators, random effects maximum likelihood in levels and first differences, and estimators that impose mean stationarity are considered for general autoregressive models with individual effects. We investigate identif ...
DOI:
10.1016/j.jeconom.2021.03.005

5. A comparative analysis of different phenological information retrieved from Sentinel-2 time series images to improve crop classification: a machine learning approach

Author:
Abdelaziz Htitiou; Abdelghani Boudhar; Youssef Lebrini; Rachid Hadria; Hayat Lionboui; Tarik Benabdelouahab
Source:
Geocarto international 2022 v.37 no.5 pp. 1426-1449
ISSN:
1752-0762
Subject:
time series analysis,   etc   ; cropland; phenology; vegetation;     Show all 4 Subjects
Abstract:
... In this study, the potential of phenological indicators derived from Sentinel-2A (S2) time series were evaluated to explore the key variables that allow identifying both cropland and crop types. Based on the derived S2 phenological metrics and fitted vegetation indices (VI), 10 feature sets were developed and assessed to discriminate different crop types via Random Forest (RF) classifier. The comp ...
DOI:
10.1080/10106049.2020.1768593

6. Harmonization of Multi-Mission High-Resolution Time Series: Application to BELAIR

Author:
Else Swinnen; Sindy Sterckx; Charlotte Wirion; Boud Verbeiren; Dieter Wens
Source:
Remote Sensing 2022 v.14 no.5 pp. -
ISSN:
2072-4292
Subject:
time series analysis,   etc   ; Landsat; anisotropy; radiometry;     Show all 4 Subjects
Abstract:
... High-resolution data are increasingly used for various applications, yet the revisit time is still low for some applications, particularly in frequently cloud-covered areas. Therefore, sensors are often combined, which raises issues on data consistency. In this study, we start from L1 to L3 data, and investigate the impact of harmonization measures, correcting for difference in radiometric gain an ...
DOI:
10.3390/rs14051163

7. Kernel-based hidden Markov conditional densities

Author:
Jan G. De Gooijer; Gustav Eje Henter; Ao Yuan
Source:
Computational statistics & data analysis 2022 v.169 pp. 107431
ISSN:
0167-9473
Subject:
time series analysis,   etc   ; algorithms; memory; models;     Show all 4 Subjects
Abstract:
... A natural way to obtain conditional density estimates for time series processes is to adopt a kernel-based (nonparametric) conditional density estimation (KCDE) method. To this end, the data generating process is commonly assumed to be Markovian of finite order. Markov processes, however, have limited memory range so that only the most recent observations are informative for estimating future obse ...
DOI:
10.1016/j.csda.2022.107431

8. Learning Latent Factors From Diversified Projections and Its Applications to Over-Estimated and Weak Factors

Author:
Jianqing Fan; Yuan Liao
Source:
Journal of the American Statistical Association 2022 v.117 no.538 pp. 909-924
ISSN:
1537-274X
Subject:
time series analysis,   etc   ; covariance; sample size;     Show all 3 Subjects
Abstract:
... Estimations and applications of factor models often rely on the crucial condition that the number of latent factors is consistently estimated, which in turn also requires that factors be relatively strong, data are stationary and weakly serially dependent, and the sample size be fairly large, although in practical applications, one or several of these conditions may fail. In these cases, it is dif ...
DOI:
10.1080/01621459.2020.1831927

9. Orbital Artifacts in Multi‐GNSS Precise Point Positioning Time Series

Author:
Radosław Zajdel; Kamil Kazmierski; Krzysztof Sośnica
Source:
Journal of geophysical research 2022 v.127 no.2 pp. e2021JB022994
ISSN:
2169-9313
Subject:
time series analysis,   etc   ; geometry; geophysics; research;     Show all 4 Subjects
Abstract:
... The time series of station coordinates derived using Global Navigation Satellite Systems (GNSS) are affected by several technique errors, influencing the studies of the geophysical processes and phenomena. Using different GNSS constellations leads to the appearance of various artificial signals with amplitudes up to several millimeters. The presence of the GNSS system‐specific artifacts is demonst ...
DOI:
10.1029/2021JB022994

10. Ultradian, circadian, and circaseptan rhythms in the patterns of usage of Facebook messenger

Author:
Ananya Diwan; Rakesh Kumar Swain; Sarojini Minz; Arti Parganiha; Atanu Kumar Pati
Source:
Biological rhythm research 2022 v.53 no.2 pp. 341-349
ISSN:
1744-4179
Subject:
time series analysis,   etc   ; biological rhythms; research;     Show all 3 Subjects
Abstract:
... The Facebook Messenger (FBM) is one of the most popular instant messaging social apps, launched by Facebook in 2010. As of October 2019, there were about 1.3 billion FBM users worldwide. In this study, we analyzed periodicities in the online activity patterns of users in FBM. We did not recruit any subjects in this study; rather four of us used our own FBM accounts to reveal the presence of any rh ...
DOI:
10.1080/09291016.2020.1747741

11. A Water Consumption Forecasting Model by Using a Nonlinear Autoregressive Network with Exogenous Inputs Based on Rough Attributes

Author:
Yihong Zheng; Wanjuan Zhang; Jingjing Xie; Qiao Liu
Source:
Water 2022 v.14 no.3 pp. -
ISSN:
2073-4441
Subject:
time series analysis,   etc   ; neural networks; prediction; water;     Show all 4 Subjects
Abstract:
... Scientific prediction of water consumption is beneficial for the management of water resources. In practice, many factors affect water consumption, and the various impact mechanisms are complex and uncertain. Meanwhile, the water consumption time series has a nonlinear dynamic feature. Therefore, this paper proposes a nonlinear autoregressive model with an exogenous input (NARX) neural network mod ...
DOI:
10.3390/w14030329

12. Harnessing NEON to evaluate ecological tipping points: Opportunities, challenges, and approaches

Author:
Ranjan Muthukrishnan; Katherine Hayes; Kristina Bartowitz; Megan E. Cattau; Brian J. Harvey; Yang Lin; Claire Lunch
Source:
Ecosphere 2022 v.13 no.3 pp. e3989
ISSN:
2150-8925
Subject:
time series analysis,   etc   ; anthropogenic activities; data collection;     Show all 3 Subjects
Abstract:
... The combination of continuing anthropogenic impact on ecosystems across the globe and the observation of catastrophic shifts in some systems has generated substantial interest in understanding and predicting ecological tipping points. The recent establishment and full operation of NEON has created an opportunity for researchers to access extensive datasets monitoring the composition and functionin ...
DOI:
10.1002/ecs2.3989

13. Is Medoid Suitable for Averaging GPS Trajectories?

Author:
Biliaminu Jimoh; Radu Mariescu-Istodor; Pasi Fränti
Source:
ISPRS international journal of geo-information 2022 v.11 no.2 pp. -
ISSN:
2220-9964
Subject:
time series analysis,   etc   ; sample size; spatial data;     Show all 3 Subjects
Abstract:
... Averaging GPS trajectories is needed in applications such as clustering and automatic extraction of road segments. Calculating mean for trajectories and other time series data is non-trivial and shown to be an NP-hard problem. medoid has therefore been widely used as a practical alternative and because of its (assumed) better noise tolerance. In this paper, we study the usefulness of the medoid to ...
DOI:
10.3390/ijgi11020133

14. Large-Scale Meta-Longitudinal Microbiome Data with a Known Batch Factor

Author:
Vera-Khlara S. Oh; Robert W. Li
Source:
Genes 2022 v.13 no.3 pp. -
ISSN:
2073-4425
Subject:
time series analysis,   etc   ; case studies; metadata; microbiome;     Show all 4 Subjects
Abstract:
... Data contamination in meta-approaches where multiple biological samples are combined considerably affects the results of subsequent downstream analyses, such as differential abundance tests comparing multiple groups at a fixed time point. Little has been thoroughly investigated regarding the impact of the lurking variable of various batch sources, such as different days or different laboratories, ...
DOI:
10.3390/genes13030392

15. Monitoring Spatio-Temporal Dynamics in the Eastern Plain Lakes of China Using Long-Term MODIS UNWI Index

Author:
Lifu Zhang; Sa Wang; Yi Cen; Changping Huang; Hongming Zhang; Xuejian Sun; Qingxi Tong
Source:
Remote Sensing 2022 v.14 no.4 pp. -
ISSN:
2072-4292
Subject:
time series analysis,   etc   ; data collection; lakes; China;     Show all 4 Subjects
Abstract:
... Monitoring the spatio-temporal dynamics of the Eastern Plain Lake (EPL) is vital to the local environment and economy. However, due to the limitations and efficiency of traditional image formats in storing and processing large amounts of images and optimal threshold adjustments are often necessary for water/non-water separation based on traditional multi-band/spectral water indexes over large area ...
DOI:
10.3390/rs14040985

16. On the semi-varying coefficient dynamic panel data model with autocorrelated errors

Author:
Honglei Wei; Hongfan Zhang; Hui Jiang; Lei Huang
Source:
Computational statistics & data analysis 2022 v.173 pp. 107458
ISSN:
0167-9473
Subject:
time series analysis,   etc   ; autocorrelation; dynamic models; prediction;     Show all 4 Subjects
Abstract:
... In nonlinear time series modeling, autocorrelation of the random errors may cause critical problems in estimation and inference. The situation becomes even worse for panel data with dynamic structure. However, most of the existing literature has not taken into account this problem. The challenge comes from the fact that the expectation of random errors conditional on lag variables is hardly to be ...
DOI:
10.1016/j.csda.2022.107458

17. Smoothing a reference pathway based on discrete exogenous projections

Author:
Taoyuan Wei
Source:
MethodsX 2022 v.9 pp. 101605
ISSN:
2215-0161
Subject:
time series analysis,   etc   ; databases; energy; methodology; models;     Show all 5 Subjects
Abstract:
... In integrated assessment studies and scenario analysis, typically we need to calibrate a model to follow an exogenous pathway, e.g., the shared socioeconomic pathways. In these exogenous pathways, the data of key variables such as GDP and energy consumption are typically provided every five or ten years. In some cases, we need a yearly smooth pathway that is consistent with such an exogenous pathw ...
DOI:
10.1016/j.mex.2021.101605

18. Spatial patterns of regional inequalities: Empirical evidence from a large panel of countries

Author:
Mihail Eva; Alexandra Cehan; Ema Corodescu-Roșca; Sébastien Bourdin
Source:
Applied geography 2022 v.140 pp. 102638
ISSN:
0143-6228
Subject:
time series analysis,   etc   ; economic development; geography; income;     Show all 4 Subjects
Abstract:
... This paper investigates regional inequality from two standpoints. First, it explores them from a global perspective by assessing the incidence of economic growth for 2867 regions from 161 countries. Results show that middle income regions had the highest growth rates after the Great Recession, whilst regions from deciles 9 and, to a lower extent, those from decile 10 had suffered the most negative ...
DOI:
10.1016/j.apgeog.2022.102638

19. Time series estimation of the dynamic effects of disaster-type shocks

Author:
Richard Davis; Serena Ng
Source:
Journal of econometrics 2022 pp. -
ISSN:
0304-4076
Subject:
time series analysis,   etc   ; economic impact; uncertainty; variance;     Show all 4 Subjects
Abstract:
... This paper provides three results for SVARs under the assumption that the primitive shocks are mutually independent. First, a framework is proposed to accommodate a disaster-type variable with infinite variance into a SVAR. We show that the least squares estimates of the SVAR are consistent but have non-standard asymptotics. Second, the disaster shock is identified as the component with the larges ...
DOI:
10.1016/j.jeconom.2022.02.009

20. Uneven trends of temperature indices during the growing season and dormancy in Serbia

Author:
Mirjana Ruml; Enika Gregorić; Gordana Matović; Slavica Radovanović; Vesna Počuča
Source:
Theoretical and applied climatology 2022 v.147 no.3-4 pp. 1277-1295
ISSN:
0177-798X
Subject:
time series analysis,   etc   ; climatology; dormancy; temperature; Serbia;     Show all 5 Subjects
Abstract:
... This paper documents the change of temperature indices for the growing season (April–September) and dormancy (October–March) in Serbia based on observations from 26 meteorological stations. The mean, maximum, and minimum daily temperatures, as well as eight extreme temperature indices, were examined. A trend analysis revealed uneven changes in the growing season and dormant temperatures during the ...
DOI:
10.1007/s00704-021-03859-8