Inversion of the western Pacific subtropical high dynamic model and analysis of dynamic characteristics for its abnormality

Abstract. Based on time series data of 500 hPa potential field from NCEP/NCAR (National Center for Environmental Forecast of American/National Center for Atmospheric Research), a novel consideration of empirical orthogonal function (EOF) time–space separation and dynamic system reconstruction for time series is introduced. This method consists of two parts: first, the dynamical model inversion and model parameter optimization are carried out on the EOF time coefficient series using the genetic algorithm (GA), and, second, a nonlinear dynamic model representing the subtropical high (SH) activity and its abnormality is established. The SH activity and its abnormal mechanism is studied using the developed dynamical model. Results show that the configuration and diversification of the SH equilibriums have good correspondence with the actual short–medium term abnormal activity of the SH. Change of SH potential field brought by the combination of equilibriums is more complex than that by mutation, and their exhibition patterns are different. The mutation behavior from high-value to low-value equilibriums of the SH in summer corresponds with the southward drop of the SH in the observed weather process. The combination behavior of the two steady equilibriums corresponds with disappearance of the "double-ridge" phenomenon of the SH. Dynamical mechanisms of these phenomena are explained.

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Time–frequency time–space LSTM for robust classification of physiological signals

Scientific Reports ◽

10.1038/s41598-021-86432-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tuan D. Pham

Keyword(s):

Time Series ◽

Network Architecture ◽

Time Series Data ◽

Short Term Memory ◽

Physiological Signals ◽

Series Data ◽

Time Frequency ◽

Time Space ◽

Deep Recurrent Neural Network

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

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Analysis of the attention to COVID-19 epidemic based on visibility graph network

Modern Physics Letters B ◽

10.1142/s0217984921503164 ◽

2021 ◽

pp. 2150316

Author(s):

Qingxiang Feng ◽

Haipeng Wei ◽

Jun Hu ◽

Wenzhe Xu ◽

Fan Li ◽

...

Keyword(s):

Public Health ◽

Time Series ◽

Time Series Data ◽

Search Behavior ◽

Series Data ◽

Global Public Health ◽

Health Crisis ◽

Time Space ◽

Epidemic Period ◽

Visibility Algorithm

Most of the existing researches on public health events focus on the number and duration of events in a year or month, which are carried out by regression equation. COVID-19 epidemic, which was discovered in Wuhan, Hubei Province, quickly spread to the whole country, and then appeared as a global public health event. During the epidemic period, Chinese netizens inquired about the dynamics of COVID-19 epidemic through Baidu search platform, and learned about relevant epidemic prevention information. These groups’ search behavior data not only reflect people’s attention to COVID-19 epidemic, but also contain the stage characteristics and evolution trend of COVID-19 epidemic. Therefore, the time, space and attribute laws of propagation of COVID-19 epidemic can be discovered by deeply mining more information in the time series data of search behavior. In this study, it is found that transforming time series data into visibility network through the principle of visibility algorithm can dig more hidden information in time series data, which may help us fully understand the attention to COVID-19 epidemic in Chinese provinces and cities, and evaluate the deficiencies of early warning and prevention of major epidemics. What’s more, it will improve the ability to cope with public health crisis and social decision-making level.

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Time is money: Dynamic-model-based time series data-mining for correlation analysis of commodity sales

Journal of Computational and Applied Mathematics ◽

10.1016/j.cam.2019.112659 ◽

2020 ◽

Vol 370 ◽

pp. 112659 ◽

Cited By ~ 3

Author(s):

Hailin Li ◽

Yenchun Jim Wu ◽

Yewang Chen

Keyword(s):

Data Mining ◽

Time Series ◽

Correlation Analysis ◽

Dynamic Model ◽

Time Series Data ◽

Series Data ◽

Time Series Data Mining ◽

Model Based

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Research on the Data-Driven Quality Control Method of Hydrological Time Series Data

Water ◽

10.3390/w10121712 ◽

2018 ◽

Vol 10 (12) ◽

pp. 1712 ◽

Cited By ~ 3

Author(s):

Qun Zhao ◽

Yuelong Zhu ◽

Dingsheng Wan ◽

Yufeng Yu ◽

Xifeng Cheng

Keyword(s):

Time Series ◽

Quality Control ◽

Confidence Intervals ◽

Time Series Data ◽

Control Method ◽

Data Driven ◽

Series Data ◽

Hydrological Data ◽

Quality Control Method ◽

Time Space

Ensuring the quality of hydrological data has become a key issue in the field of hydrology. Based on the characteristics of hydrological data, this paper proposes a data-driven quality control method for hydrological data. For continuous hydrological time series data, two combined forecasting models and one statistical control model are constructed from horizontal, vertical, and statistical perspectives and the three models provide three confidence intervals. Set the suspicious level based on the number of confidence intervals for data violations, control the data, and provide suggested values for suspicious and missing data. For the discrete hydrological data with large time-space difference, the similar weight topological map between the neighboring stations is established centering on the hydrological station under the test and it is adjusted continuously with the seasonal changes. Lastly, a spatial interpolation model is established to detect the data. The experimental results show that the quality control method proposed in this paper can effectively detect and control the data, find suspicious and erroneous data, and provide suggested values.

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Detecting Forest Disturbance in Northeast China from GLASS LAI Time Series Data Using a Dynamic Model

Remote Sensing ◽

10.3390/rs9121293 ◽

2017 ◽

Vol 9 (12) ◽

pp. 1293 ◽

Cited By ~ 3

Author(s):

Jian Wang ◽

Jindi Wang ◽

Hongmin Zhou ◽

Zhiqiang Xiao

Keyword(s):

Time Series ◽

Dynamic Model ◽

Northeast China ◽

Time Series Data ◽

Forest Disturbance ◽

Series Data

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Pooling Cross Section and Time Series Data in the Estimation of a Dynamic Model: The Demand for Natural Gas

Econometrica ◽

10.2307/1909771 ◽

1966 ◽

Vol 34 (3) ◽

pp. 585 ◽

Cited By ~ 482

Author(s):

Pietro Balestra ◽

Marc Nerlove

Keyword(s):

Time Series ◽

Natural Gas ◽

Dynamic Model ◽

Cross Section ◽

Time Series Data ◽

Series Data

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Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

PsycEXTRA Dataset ◽

10.1037/e634372013-001 ◽

2013 ◽

Author(s):

Stephen J. Tueller ◽

Richard A. Van Dorn ◽

Georgiy Bobashev ◽

Barry Eggleston

Keyword(s):

Time Series ◽

Data Analysis ◽

Exploratory Data Analysis ◽

Time Series Data ◽

Series Data ◽

Exploratory Data

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Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

Jurnal Riset Manajemen Sekolah Tinggi Ilmu Ekonomi Widya Wiwaha Program Magister Manajemen ◽

10.32477/jrm.v1i2.72 ◽

2017 ◽

Vol 1 (2) ◽

pp. 177-191

Author(s):

Rizki Rahma Kusumadewi ◽

Wahyu Widayat

Keyword(s):

Time Series ◽

Exchange Rate ◽

Money Supply ◽

Time Series Data ◽

The United States ◽

Economic Conditions ◽

Series Data ◽

Arch Model ◽

United States Dollar ◽

The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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